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Interactive Communication

2008-04-21T21:11:44Z

Vanlehn: /* Tell vs. elicit */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, VanLehn & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2005)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly, & Treacy, 2006)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2007)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

*[[Ringenberg_Ill-Defined_Physics|Eliciting missing information for solving ill-defined physics problems. (Ringenberg & VanLehn, 2008)]]
[[Category:Cluster]]

McLaren et al - Conceptual Learning in Chemistry

2008-04-21T20:59:53Z

Vanlehn: /* Findings */

==Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support==

Bruce M. McLaren, Nikol Rummel, Andreas Harrer, Hans Spada, Niels Pinkwart

===Overview===

PI: Bruce M. McLaren

Co-PIs: Nikol Rummel, Andreas Harrer, Hans Spada, Niels Pinkwart

Others who have contributed 160 hours or more:

* Dimitra Tsovaltzi, University of Saarland, Germany, experimental design and execution
* Isabel Braun, Freiburg University, Germany, experimental design and execution
* Oliver Scheuer, University of Saarland, Germany, data mining and programming
* Roger Miller, University of Saarland, Germany, programming

The goal of this one-year project is to build/integrate technology and perform small-scale lab tests in preparation for in vivo studies in later years.

===Abstract===

Chemistry students, like students in physics, mathematics, and other technical disciplines, often learn to solve problems algorithmically, applying well-practiced procedures to textbook problems. But often these students do not understand the underlying conceptual aspects of the problems they solve algorithmically. An important setting for promoting [[conceptual knowledge]] in chemistry is the laboratory, where students must apply not only pre-defined problem solving procedures, but must also plan experiments, hypothesize outcomes, conduct and monitor experiments, and evaluate outcomes. In the PSLC Chemistry LearnLab, the Virtual Laboratory (VLab) is the online software environment used to simulate a real chemistry laboratory and assist students in their conceptual understanding of chemistry. However, the VLab on its own is not enough. We propose to further assist chemistry students in gaining [[conceptual knowledge]], first, through having pairs of students [[collaboration|collaborate]] on problems, assisted by computer-mediated collaboration scripts that extend the VLab and, later, through dynamic adaptation of those collaboration scripts.
 
In this one-year project, we conducted a pilot study (STUDY 1) comparing how singles and dyads solve chemistry problems with the VLab with and without [[collaboration scripts|scripts]]. We used the results to inform the design of a computer-mediated [[Collaborative_learning_environment|collaborative environment]] around the VLab, using a collaborative software tool called FreeStyler. In a subsequent small-scale study (STUDY 2) we compared an adaptive and a non-adaptive version of the system. The adaptation was realized by a human wizard sending feedback to the students following a predefined model. A qualitative data analysis of this study revealed a tendency for the dyads that received the adaptive feedback to improve [[Collaboration_skill|collaborative skills]] and be more motivated than the non-adative dyads.
 
The one-year PSLC project will provide the foundation for an externally-funded project, still conducted within the PSLC Chemistry LearnLab, in which we will perform full-scale in vivo studies to test the hypotheses that (1) [[collaboration]], supported by [[collaboration scripts]], can promote the creation and strengthening of conceptual chemistry [[knowledge component|knowledge components]] and (2) that dynamic adaptation of the [[collaboration scripts]] can further improve that learning.

===Glossary===

See [[:Category:Scripted Collaborative Problem Solving|Scripted Collaborative Problem Solving Glossary]]

===Research Questions===

Does [[collaboration]] – and in particular adaptive [[collaboration scripts|scripted collaboration]] – improve students’ [[robust learning]], and in particular conceptual learning, in the domain of chemistry?

Does the adaptive [[collaboration scripts|script]] approach improve students’ collaboration, and does this result in more [[robust learning]] of chemistry content?

===Hypothesis===

These research questions led us to the following two hypotheses:

;H1
:Computer-mediated collaboration, facilitated by collaboration scripts and added to experimental exercises within the stoichiometry course, can promote the creation and strengthening of conceptual stoichiometry knowledge components.

;H2
:Computer-mediated collaboration, facilitated by adaptive collaboration scripts and added to experimental exercises within the stoichiometry course, can promote the creation and strengthening of conceptual stoichiometry knowledge components.

===Background and Significance===

A central issue in chemistry education is teaching students to problem solve conceptually rather than simply apply mathematical equations. Research in chemistry education has shown that students tend to learn and solve problems “algorithmically” but often do not grasp the deeper conceptual aspects of chemistry and reasoning necessary to be more creative and flexible problem solvers (Gabel & Bunce, 1994; Bodner & Herron, 2002). Dave Yaron, the chair of the Pittsburgh Science of Learning Center (PSLC)’s Chemistry LearnLab, has expressed a similar view in observing his students, saying, “many students learn the mathematical tools necessary to solve chemistry problems but don’t know when to appropriately apply those tools” (Personal communication between McLaren and Yaron February 27, 2006; also discussed in Yaron et al, 2003). The phenomenon that learners have problems transferring instructed procedures to new problems due to a lack of conceptual understanding has been observed and investigated also in other domains, for example, math (Singley & Anderson, 1989).

The difficulty chemistry students have can be viewed as a transfer problem, an important area of investigation in the PSLC’s emerging theory of [[robust learning]]. In particular, while chemistry students often have success on problems that are very similar to ones illustrated in a textbook or demonstrated in a classroom, they tend to struggle with problems that could be solved with similar techniques but are not obviously of the same type (e.g., the source and target problems don’t share surface features). This difficulty is due to students lacking the [[conceptual knowledge]] of chemistry to recognize similar core problems that come in “different clothes.”

There is some descriptive evidence in chemistry education research indicating that collaborative activities can improve conceptual learning in chemistry (e.g. Towns & Grant, 1998; Fasching & Erickson, 1985). Other studies, while not focused specifically on conceptual versus algorithmic learning, have demonstrated increased performance as well as motivational benefits of collaborative learning in chemistry (Ross & Fulton, 1994). On the other hand, none of these collaborative learning studies in chemistry was a randomized controlled experiment. In general, there is a lack of controlled experimentation on the potential benefits of collaborative learning in chemistry. However, such evidence exists in math (Berg, 1993, 1994), physics (Hausmann, Chi & Roy, 2004; Ploetzner, Fehse, Kneser, & Spada, 1999), or scientific experimentation (Teasley, 1995). Research in collaborative learning has shown promise in helping students to more deeply process information and thus improve their conceptual learning. A few different mechanisms are accountable for the benefits of collaborative activities, like giving explanations to the partner, receiving help from the partner after making a mistake or asking for help, and co-constructing or jointly negotiating knowledge (Hausmann, Chi, & Roy, 2004; Ploetzner, Dillenbourg, Preier, & Traum, 1999; Webb, 1989; Webb, Trooper, & Fall, 1995). In sum, results from this research lead us to the assumption that it would be worthwhile investigating the advantages of collaborative activities on the acquisition of robust, transferable conceptual knowledge in controlled experimental studies in chemistry.

In this project, we will test the hypothesis that a computer-supported collaborative learning system can help students improve their conceptual understanding of chemistry. Our goal is to help students actively process the material they encounter, moving them away from the mechanical, algorithmic approach taken by many chemistry students. In terms of the PSLC theoretical framework, we assume that the collaborative situation creates additional [[learning events]] through the above cited mechanisms of receiving help, giving explanations, and co-constructing knowledge. In addition, the collaborative setting may increase the likelihood that students capitalize on the learning events offered by the domain setting (i.e. the chemistry learning environment). That is, collaborative interactions can increase the likelihood of particular path choices in the learning event space that benefit learning. To test our hypothesis, we will develop collaborative extensions to the Virtual Lab (VLab) and compare individual learning in the course with scaffolded collaborative learning.

We believe that it might be best to scaffold collaboration in an adaptive fashion, emphasizing and fading structured support for collaboration according to the particular needs of the specific collaborators. Past studies suggest that different students, under different circumstances, may benefit from different types of collaboration support; thus, a collaborative learning system that can adapt its support might prove quite powerful. One study that we are aware of, in which adaptive, strategic “prompts” in a collaborative system were shown to lead to productive collaboration and support for learning, is the work of Gweon, Rosé, Carey, & Zaiss (2006). While interest in adaptive collaborative learning systems is on the rise in the computer-supported collaborative learning (CSCL) community (Soller, Jermann, Muehlenbrock, & Martinez, 2005), little progress has yet been made on the implementation of adaptive support.

This PSLC project is only the first year of what we intend to be a four-year project. Our goal in the first year will be to
* analyze problem solving in the stoichiomery course, both individual and collaborative,
* perform small-scale (i.e., small N) lab studies to experiment with the general hypothesis that collaboration support, in the form of collaboration scripts, can enhance conceptual learning of stoichiometry,
* develop a prototype of an online collaboration system that integrates the VLab, an online simulation of chemistry experimentation and an integral part of the PSLC Chemistry LearnLab (Yaron et al, 2003), with Cool Modes, a software tool that facilitates computer-mediated collaboration and simulation (Pinkwart, 2003).
The technical work will then set the stage for the adaptive collaboration effort we propose in the second phase of the project.

The second phase of the project, after the initial year of PSLC seed funding, will focus on full-scale in vivo experimentation, first testing collaboration scripts and, later, adaptive collaboration scripts. The second phase of the project will also involve continued technical development to support dynamic adaptation of collaborative support, continuing work on the bootstrapping technique, described above, and investigating the use of cognitive tutoring techniques to support collaboration.

===Independent Variables===

;STUDY 1
* scripted (paper-based script) vs unscripted
* singles vs dyads (students sitting next to each other)

;STUDY 2
* adaptive scripted vs non-adaptive scripted

===Dependent Variables===

;STUDY 1
* Learning
** Retention (''[[Normal post-test]]'')
* Problem solving performance and efficiency
** Average problem solving time
** Average number of problems solved
** Average number of VLab actions performed (per action type)

;STUDY 2
* Learning
** Retention (''[[Normal post-test]]'')
** ''[[Transfer]]'' (conceptual transfer questions)
* Problem solving behavior (measured by counting the number of occurrences of behavior classes)
** good and bad script practice (behavior according to the script)
** good and bad collaboration practice
** progress during a session with respect to script and collaboration practice (improved, deteriorated, stable)

===Findings===

Due to the small sample size of both studies we cannot report meaningful statistical results. Our findings are therefore preliminary and of anecdotal nature.

;STUDY 1
The pre-posttest data (Table 1) reveal no substantial differences in the gain scores between the four conditions. The scripted dyad condition performed the poorest in the pre-post test analysis; it was the only group that scored lower on average on the posttest than the pretest. Moreover, in the interviews after the problem solving the scripted dyads unanimously expressed the view that the script was not helpful. However, our results also indicate that, in spite of the perceived constraints of the script, it was nonetheless helpful: An additional log file analysis revealed that the scripted conditions were more efficient in solving problems because they performed far fewer “mix solution” actions in the VLab. That is, they took less steps to achieve similar results. At least anecdotally, collaboration was also helpful, as the scripted and unscripted dyads conditions solved 12 problems in total, while the singles solved only 8 problems (Table 2).

'' Table 1: Pre-Posttest results ''
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Condition''' || '''N''' || '''Pretest (SD)''' || '''Posttest (SD)''' || '''Gain (SD)'''
|-
| '''Scripted dyads''' || 8 || 4.44 (0.82) || 4.31 (0.88) || -0.13 (0.52)
|-
| '''Scripted singles''' || 4 || 3.88 (1.11) || 4.38 (1.25) || 0.50 (1.48)
|-
| '''Unscripted dyads''' || 8 || 3.56 (0.62) || 4.06 (1.37) || 0.50 (1.49)
|-
| '''Unscripted singles''' || 4 || 4.38 (0.63) || 4.38 (0.63) || 0.00 (1.08)
|}
 

'' Table 2: Problem solving performance''
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Condition''' || '''N''' || '''Avg. time problem type 1 (DNA)''' || '''Avg. time problem type 2 (Oracle)''' || '''# problems solved type 1 (DNA)''' || '''# problems solved type 2 (Oracle)'''
|-
| '''Scripted dyads''' || 4 || 19min || 43min || 3 || 2
|-
| '''Scripted singles''' || 4 || 20min || 39min || 3 || 1
|-
| '''Unscripted dyads''' || 4 || 18min || 27min || 4 || 3
|-
| '''Unscripted singles''' || 4 || 21min || 36min || 3 || 2
|}
 

;STUDY 2
The test results showed a tendency of better conceptual understanding in the adaptive condition. With a highest possible score of 6 points, the adaptive condition mean was M=4.6 (SD 1.63) whereas the non-adaptive condition scored in average M=3.5 (SD 2.81). A process analysis (Table 3) based on video recordings showed a big difference in terms of “good script practice” and “good collaborative practice” in favor of the adaptive dyads. “Bad collaborative practice” is also considerably less in the adaptive condition. Looking at tendencies during sessions we see that students’ collaboration and scripting practice improved for all of the three dyads in the adaptive conditions whereas it remained stable (1 dyad) or even deteriorated (2 dyads) in the non-adaptive condition.

'' Table 3: Process analysis results''
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''Analysis category''' || '''Avg. counts adaptive(SD)''' || '''Avg. counts non-adaptive(SD)'''
|-
| '''Good scripting practice''' || 6.33 (2.51) || 5.00 (2.64)
|-
| '''Bad scripting practice''' || 4.33 (3.21) || 7.33 (2.30)
|-
| '''Good collab. practice''' || 5.66 (1.15) || 3.00 (1.00)
|-
| '''Bad collab. practice''' || 2.00 (1.00) || 1.66 (1.15)
|}
 

===Explanation===

;STUDY 1
Scripted dyads might have been [[Cognitive_load|overloaded]] by the simultaneous demands of getting acquainted with a computer-based learning environment, [[Coordination|collaborating]] with a partner, attending to a [[Collaboration_scripts|script]], and solving a task (Rummel & Spada, 2005b; Rummel, Spada, & Hauser, 2006). The fact that they had to work with a paper-based script, which involved a lot of reading and monitoring of their own activities, increased this effect. As a consequence, we simplified the task in STUDY 2 by using a less complicated script and introducing individual (non-collaborative) script phases.

;STUDY 2
Our qualitative data analysis suggests that adaptive feedback can improve [[Collaboration_skill|collaborative skills]] and might have positive motivational effects. Given the small N, larger scaled studies are needed to get results which are statistically more meaningful.

=== Connections to Other PSLC Studies===

* The use of [[collaboration scripts]] in this project is clearly similar to that of the [[Rummel Scripted Collaborative Problem Solving | PSLC McLaren/Rummel Algebra Collaboration Studies]]. Here, however, we are after promoting conceptual learning, rather than domain and collaborative learning.

===Annotated Bibliography===

*Tsovaltzi, D., McLaren, B.M., Rummel, N., Scheuer, O., Harrer, A., Pinkwart, N. & Braun, I., (accepted). CoChemEx: Supporting Conceptual Chemistry Learning via Computer-Mediated Collaborative Scripts. Accepted as a poster paper at the 9th International Conference on Intelligent Tutoring Systems (ITS-08). To take place June 23-27, 2008, Montreal, Canada.
*Harrer, A., Pinkwart, N., McLaren, B.M., & Scheuer, O., (accepted). How Do We Get the Pieces to Work Together? A New Software Architecture to Support Interoperability between Educational Software Tools. Accepted as a poster paper at the 9th International Conference on Intelligent Tutoring Systems (ITS-08). To take place June 23-27, 2008, Montreal, Canada
*McLaren, B.M., Rummel, N., Tsovaltzi, D., Braun, I., Scheuer, O., Harrer, A., and Pinkwart, N. (2007). The CoChemEx Project: Conceptual Chemistry Learning through Experimentation and Adaptive Collaboration. In the Proceedings of the Workshop on 'Emerging Technologies for Inquiry Based Learning in Science' , AIED-07. (p. 36-48). Los Angeles (CA). [[Image:AIED-07-InqLearnWS-CoChemEx.pdf|pdf file]]

===References===

*Aleven, V., McLaren, B. M., Roll, I. and Koedinger, K. R. (2004). Toward Tutoring Help Seeking: Applying Cognitive Modeling to Meta-Cognitive Skills; In the Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004).
*Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167-207.
*Anderson, J. (1990). Cognitive Psychology and its Implications. W. H. Freeman and Company, New York.
*Bell, T., Slotta, J., Schanze, S. (to appear). Perspectives on collaborative inquiry learning: An International network for research, curriculum and technology. Special Issue of the International Journal of Science Education, to appear at the end of 2006.
*Berg, K. F. (1993, April). Structured cooperative learning and achievement in a high school mathematics class. Paper presented at the Annual Meeting of the American Educational Research Association. Atlanta, GA.
*Berg, K. F. (1994, April). Scripted Cooperation in High School Mathematics: Peer interaction and achievement. Paper presented at the Annual Meeting of the American Educational Research Association. New Orleans, Louisiana.
*Bodner, G. M., & Herron, J. D. (2002). Problem-Solving in Chemistry. In (J.K. Gilbert et al, Eds.) Chemical Education: Towards Research-Based Practice. Kluwer Academic Publishers. 235-266.
*BouJaoude, S. & Barakat, H. (2003). Students’ Problem Solving Strategies in Stoichiometry and their Relationships to Conceptual Understanding and Learning Approaches, Electronic Journal of Science Education, 7 (3).
*Chi, M. T. H. (2005). Commonsence Conceptions of Emergent Processes: Why Some Misconceptions are Robust. The Journal of the Learning Sciences, 14(2), 161-199.
*de Jong, T. (2006). Scaffolds for computer simulation based scientific discovery learning. In J. Elen, R. E. Clark (Eds.) Dealing with complexity in learning environments (pp. 107-128). London: Elsevier Science Publishers.
*Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL (pp. 61-91). Heerlen: Open Universiteit Nederland.
*Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1995). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp. 189-211). Oxford: Elsevier/Pergamon.
*Fasching, J. L. & Erickson, B. L. (1985). Group discussions in the chemistry classroom and the problem-solving skills of students. Journal of Chemical Education, 62, 842-848.
*Gabel, D. L. (1981). Facilitating problem solving in high school chemistry. Indiana University, School of Education, Bloomington. (ERIC Document Reproduction Service No. ED 210 192).
*Gabel, D. L. & Samuel, K. V. (1986). High school students’ ability to solve molarity problems and their analog counterparts. Journal of Research in Science Teaching, 23, 165-176.
*Gabel, D. L., & Bunce, D. M. (1994). Research on Problem Solving: Chemistry. In D. L. Gabel (Ed.), Handbook of Research on Science Teaching and Learning. New York: Simon & Schuster. 301-326.
*Gweon, G., Rosé, C., Carey, R. & Zaiss, Z. (2006). Providing Support for Adaptive Scripting in an On-Line Collaborative Learning Environment. Presented at CHI 2006.
*Harrer, A., McLaren, B. M., Walker, E., Bollen, L., and Sewall, J. (2005). Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Directions; In the Proceedings of the 12th International Conference on Artificial Intelligence and Education (AIED-05), Amsterdam, the Netherlands, July 2005.
*Hausmann, R. G., Chi, M. T. H. & Roy, M. (2004). Learning from collaborative problem solving: An analysis of three hypothesized mechanisms. In K. D. Forbus, D. Gentner & T. Regier (Eds.), 26nd annual Conference of the Cognitive Science Society (pp. 547-552). Mahwah, NJ: Lawrence Erlbaum.
*Hoppe, H. U. (2004). Collaborative mind tools. In M. Tokoro & L. Steels (Eds.), An earning zone of one’s own - sharing representations and flow in collaborative learning environments (p. 223-234). Amsterdam, The Netherlands: IOS Press.
*King, A. (1998). Transactive peer tutoring: Distributing cognition and metacognition. Educational Psychology Review, 10, 57-74.
*King, A. (1991). Improving lecture comprehension: Effects of a metacognitive strategy. Applied Cognitive Psychology, 5, 331-346.
*Klahr, D. & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1-48.
*Koedinger, K. R., Klahr, D., Perfetti, C., & VanLehn, K. (2004). Pittsburgh science of learning center: Studying robust learning with learning experiments in real classrooms. NSF Proposal, submitted and granted.
*Koedinger, K., Aleven, V., Heffernan, N., McLaren, B. M., & Hockenberry, M. (2004). Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration; In the Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS-2004).
*Kollar, I., Fischer, F., & Hesse, F. W. (2003). Cooperation scripts for computer-supported collaborative learning. In B. Wasson, R. Baggetun, U. Hoppe & S. Ludwigsen (Eds.), Proceedings of the Computer Support for Collaborative Learning (CSCL) 2003 Conference (pp. 59-61). Bergen, Norway: InterMedia, University of Bergen.
*Koper, R. & Tattersall, C. (ed): Learning Design: A Handbook on Modelling and Delivering Networked Education and Training, Springer Verlag (2005).
*Kramarski, B. (2004). Making sense of graphs: Does metacognitive instruction make a difference on students’ mathematical conceptions and alternative conceptions? Learning and Instruction, 14(6), 593-619.
*Kuhn, M., Hoppe, H. U., Lingnau, A., & Fendrich, M. (2004). Evaluation of exploratory approaches in learning probability based on computational modelling and simulation. In Pedro Isaias, Kinshuk, and Demetrios G. Sampson, editors, Proceedings of the IADIS conference of Cognition and Exploratory Learning in Digital Age (CELDA), pages 83–90, *Lisbon, Portugal. IADIS Press.
*Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495-523.
*Lingnau, A., Kuhn, M., Harrer, A., Hofmann, D., Fendrich, M. & Hoppe, H. U. (2003). Enriching traditional classroom scenarios by seamless integration of interactive media. In Vladan Devedzic, J. Michael Spector, Demetrios G. Sampson, and Kinshuk, editors, Proceedings of the 3rd IEEE International Conference on Advanced Learning Technologies (ICALT), pages 135–139, Los Alamitos, CA (USA). IEEE Press.
*Lythcott, J. (1990). Problem Solving and Requisite Knowledge of Chemistry. Journal of Chemical Education, 67(3). 248-252.
*McLaren, B. M., Lim, S., Gagnon, F., Yaron, D., & Koedinger, K. R. (2006). Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based Intelligent Tutor; Accepted for presentation at the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, June 26-30, 2006.
*McLaren, B. M., Bollen, L., Walker, E., Harrer, A., and Sewall, J. (2005). Cognitive Tutoring of Collaboration: Developmental and Empirical Steps Toward Realization; In the Proceedings of the Conference on Computer Supported Collaborative Learning (CSCL-05), Taipei, Taiwan in May/June 2005.
*McLaren, B. M., Koedinger, K. R., Schneider, M., Harrer, A., & Bollen, L. (2004). Toward Cognitive Tutoring in a Collaborative, Web-Based Environment; In Engineering Advanced Web Applications, From the Proceedings in Connection with the 4th International Conference on Web Engineering (ICWE 2004), Munich, Germany, 28-30 July, 2004.
*Mevarech, Z. R., & Kramarski, B. (2003). The effects of metacognitive training versus worked-out examples on students’ mathematical reasoning. British Journal of Educational Psychology, 73(4), 449-471.
*Moschkovich, J. N. (1996). Moving up and getting steeper: Negotiating shared descriptions of linear graphs. Journal of the Learning Sciences, 5(3), 239-277.
*Nurrenbern, S. C. & Pickering, M. (1987). Conceptual learning versus problem solving: is there a difference? Journal of Chemical Education, 64, 508-510.
*O’Donnell, A. M. (1999). Structuring dyadic interaction through scripted cooperation. In A. M. O’Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 179-196). Mahwah, NJ: Lawrence Erlbaum Associates.
*Phelps, A. J. (1996). Teaching to Enhance Problem Solving. Journal of Chemical Education,73:4, 301-303.
*Pinkwart, N. (2003) A Plug-In Architecture for Graph Based Collaborative Modelling Systems. In U. Hoppe, F. Verdejo & J. Kay (eds.): Proceedings of the 11th Conference on Artificial Intelligence in Education, 535-536.
*Pinkwart, N. (2005). Collaborative Modeling in Graph Based Environments. Berlin (Germany), dissertation.de - Verlag im Internet.
*Pinkwart, N., Aleven, V., Ashley, K., & Lynch, C. (2006, to appear). Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques. To appear in Proceedings of the 8th International Conference on Intelligent Tutoring Systems.
*Plötzner, R., Dillenbourg, P., Preier, M., & Traum, D. (1999). Learning by explaining to oneself and to others. In P. Dillenbourg (Ed.), Collaborative learning. Cognitive and computational approaches (pp. 103-121). Amsterdam: Pergamon.
*Plötzner, R., Fehse E., Kneser, C., & Spada, H. (1999). Learning to relate qualitative and quantitative problem representations in a model-based setting for collaborative problem-solving. The Journal of the Learning Sciences, 8, 177–214.
*Robinson, W. R. & Niaz, M. (1991). Performance based on instruction by lecture or by interaction and its relationship to cognitive variables. International Journal of Science Education, 13, 203-215.
*Ross, M. & Fulton, R. (1994). Active learning strategies in the analytical chemistry classroom. Journal of Chemical Education, 71, 141-143.
*Rummel, N., Spada, H., & Hauser, S. (2006). Learning to Collaborate in a Computer-Mediated Setting: Observing a Model Beats Learning from Being Scripted. Accepted for presentation at the 7th International Conference of the Learning Sciences, June 27 - July 1 2006, Indiana University, Bloomington IN.
*Rummel, N. & Spada, H. (2005a). Can people learn computer-mediated collaboration by following a script? In Fischer, F., Mandl, H., Haake, J. & Kollar, I. (Eds.), Scripting computer-supported communication of knowledge Cognitive, computational, and educational perspectives. Dordrecht, NL: Kluwer.
*Rummel, N. & Spada, H. (2005b). Learning to collaborate: An instructional approach to promoting collaborative problem solving in computer mediated settings. Journal of the Learning Sciences, 14(2).
*Sawrey, B. A. (1990). Concept learning versus problem-solving: revisited. Journal of Chemical Education, 67, 253-254.
*Singley & Anderson (1989). The transfer of cognitive skill. Cambridge, MA: Harvard University Press.
*Slavin, R. E. (1996). Research on Cooperative Learning and Achievement: What we know, what we need to know. Contemporary Educational Psychology, 21(1), 43-69.
*Soller, A., Jermann, P., Muehlenbrock, M. & Martinez, A. (2005). From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning. International Journal of Artificial Intelligence in Education, 15(4), 261-290.
*Teasley, S. D. (1995). The role of talk in children’s peer collaborations. Developmental Psychology, 31(2), 207-220.
*Tingle, J. B. & Good, R. (1990). Effects of Cooperative Grouping on Stoichiometry Problem Solving in High School Chemistry. Journal of Research in Science Teaching, 27, 671-683.
*Towns, M. & Grant, E. (1998). ‘I believe I will go out of this class actually knowing something:’ Cooperative learning activities in physical chemistry. Journal of Research in Science Teaching, 34, 819-835.
*van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E., & Manlove, S. (2005). Co-lab: Research and development of an on-line learning environment for collaborative scientific discovery learning. Computers in Human Behavior, 21, 671-688.
*Verdejo, M.F., Barros, B., Gómez Antón, R. & Read, T. (2003). The design and implementation of experimental collaborative learning in a Distance Learning context. In Proceedings of ITHET03, 4th international conference on Information Technology Based Higher Education.
*Webb, N. M. (1989). Peer interaction and learning in small groups. International Journal of Education Research, 13, 21-39.
*Webb, N.M., Troper, J.D., & Fall, R. (1995). Constructive activity and learning in collaborative small groups. Journal of Educational Psychology, 87, 406-423.
*Webb, N. M., & Mastergeorge, A. M. (2003). The development of students’ helping behavior and learning in peer-directed small groups. Cognition and Instruction, 21(4), 361-428.
*Wittenbaum, G. M., & Stasser, G. (1996). Management of information in small groups. In J. L. Nye & A. M. Brower (Eds.), What’s social about social cognition? Research on socially shared cognition in small groups (pp. 3-28). Newbury Park, CA: Sage Publications.
*Yaron, Evans, & Karabinos (2003). Scenes and Labs Supporting Online Chemistry. Paper presented at the 83rd Annual AERA National Conference, April 2003.
*Yarroch, W. J. (1985). Students’ understanding of chemical equation balancing. Journal of Research in Science Teaching, 22, 449-459.

Interactive Communication

2008-04-21T20:37:15Z

Vanlehn: /* Collaboration */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, VanLehn & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2005)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly, & Treacy, 2006)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2007)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

*[[Ringenberg_Ill-Defined_Physics|Eliciting missing information for solving ill-defined physics problems. (Ringenberg & VanLehn, 2008)]]
[[Category:Cluster]]

Interactive Communication

2008-04-21T20:08:29Z

Vanlehn: /* Collaboration */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2005)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly, & Treacy, 2006)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2007)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

*[[Ringenberg_Ill-Defined_Physics|Eliciting missing information for solving ill-defined physics problems. (Ringenberg & VanLehn, 2008)]]
[[Category:Cluster]]

Interactive Communication

2008-04-21T20:05:13Z

Vanlehn: /* Collaboration */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[Walker|Adaptive Assistance for Peer Tutoring (Walker, Rummer, Koedinger)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2005)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly, & Treacy, 2006)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2007)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

*[[Ringenberg_Ill-Defined_Physics|Eliciting missing information for solving ill-defined physics problems. (Ringenberg & VanLehn, 2008)]]
[[Category:Cluster]]

File:Ic cluster table.pdf

2008-04-14T19:08:11Z

Vanlehn:

Interactive Communication

2008-04-14T15:17:47Z

Vanlehn: /* The PSLC Interactive Communication cluster */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2006)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy, 2007)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2008)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

*[[Plateau_study|What is the optimal level of interaction during learning from problem solving? (Hausmann, van de Sande, & VanLehn, 2008)]]

[[Category:Cluster]]

Interactive Communication

2008-04-14T14:56:03Z

Vanlehn: /* Questioning */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly, 2006)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy, 2007)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly, 2008)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Interactive Communication

2008-04-14T14:47:39Z

Vanlehn: /* Tell vs. elicit */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

*[[McLaren_et_al_-_Conceptual_Learning_in_Chemistry|Supporting Conceptual Learning in Chemistry through Collaboration Scripts and Adaptive, Online Support (McLaren, Rummel, Harrer, Spada, & Pinkwart)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy)]]

*[[Extending Reflective Dialogue Support (Katz & Connelly)|Extending Reflective Dialogue Support (Katz & Connelly)]]

*[[Self-explanation: Meta-cognitive vs. justification prompts|Self-explanation: Meta-cognitive vs. justification prompts (Hausmann, van de Sande, Gershman, & VanLehn, 2008]])

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in the Refinement & Fluency cluster, and relevant to Knowledge Component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Coordinative Learning

2008-04-14T14:34:58Z

Vanlehn: /* Examples and Explanations */

= The PSLC Coordinative Learning cluster =

== Abstract ==
The studies in the Coordinative Learning cluster tend to focus on varying ''a)'' the types of information available to learning or ''b)'' the instructional methods that they employ. In particular, the studies focus on the impact of having learners coordinate two or more types. Given that the student has multiple [[sources]]/methods available, two factors that might impact learning are:

*What is the relationship between the content in the two sources or the content generated by the two methods? Our hypothesis is that the two sources or methods facilitate [[robust learning]] when a [[knowledge component]] is difficult to understand or absent in one and is present or easier to understand in the other.
*When and how does the student coordinate between the two sources or methods? Our hypothesis is that students should be encouraged to compare the two, perhaps by putting them close together in space or time.

At the micro-level, the overall hypothesis is that robust learning occurs when the [[learning event space]] has target paths whose [[sense making]] difficulties complement each other (as expressed in the first bullet above) and the students make path choices that take advantage of these [[complementary]] paths (as in the second bullet, above). This hypothesis is just a specialization of the [[Root_node|general PSLC hypothesis]] to this cluster.

The matrix below shows how studies in this cluster (pages for these studies can be found Descendants section below) either test or make use of various [[instructional method|instructional methods]] or treatments. When a study tests an instructional method a "v" is one shown in the appropriate cell to indicate that that method is '''varied''' in the study, that is, the [[robust learning]] gains of an experimental condition that receives this method are contrasted with those of an otherwise equivalent control condition that does not receive this method. In this case (when a "v" is present), the study tests the [[InstructionalPrinciples|instructional principle]] indicated in the column. When a cell contains a "b" it indicates that '''both''' the experimental and control conditions use this instructional method (or employ this instructional principle). In this case, the study is not a true experimental test of the principle.

 <center>[[Image:cl-theory.jpg]]</center>

== Glossary ==
[[:Category:Coordinative Learning|Coordinative Learning]] glossary.

*'''[[Co-training]]'''
*'''[[Complementary]]'''
*'''[[Conceptual tasks]]'''
*'''[[Contiguity]]'''
*'''[[Coordination]]'''
*'''[[Ecological control group]]'''
*'''[[External representations]]'''
*'''[[Input sources ]]'''
*'''[[Instructional method]]'''
*'''[[Multimedia sources]]'''
*'''[[Procedural tasks]]'''
*'''[[Self-explanation]]'''
*'''[[Self-supervised learning]]'''
*'''[[Sources]]'''
*'''[[Strategies]]'''
*'''[[Unlabeled examples]]'''

== Research questions ==

When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?

Two sub-groups of coordinative learning studies are exploring these more specific questions:

=== Visualizations and Multi-modal sources ===

When does adding visualizations or other multi-modal input enhance robust learning and how do we best support students in coordinating these sources?

=== Examples and Explanations ===

When and how should example study be combined and coordinated with problem solving to increase robust learning? When and how should explicit explanations be added or requested of students before, during, or after example study and problem solving practice?

== Independent variables ==

*Content of the sources (e.g., pictures, diagrams, written text, audio, animation) or the encouraged lines of reasoning (e.g., example study, self-explanation, conceptual task, procedural task) and combinations

*Instructional activities designed to engage students in [[coordination]] (e.g., conceptual vs. [[procedural]] exercises, contiguous presentation of sources, [[self-explanation]])

See [[:Category:Independent Variables]]

== Dependent variables ==
[[Normal post-test]] and measures of [[robust learning]].

== Hypotheses ==
When students are given sources/methods whose [[sense making]] difficulties are complementary and they are engaged in coordinating the sources/methods, then their learning will be more robust than it would otherwise be.

== Explanation ==

There are both [[sense making]] and [[foundational skill building]] explanations. From the sense making perspective, if the sources/methods yield complementary content and the student is engaged in coordinating them, then the student is more likely to successfully understand the instruction because if a student fails to understand one of the sources/methods, he can use the second to make sense of the first. From a foundational skill building perspective, attending to both sources/methods simultaneously associates [[features]] from both with the learned knowledge components, thus potentially increasing [[feature validity]] and hence [[robust learning]].

== Descendents ==

=== Visualizations and Multi-modal sources ===
*[[Contiguous Representations for Robust Learning (Aleven & Butcher)]]
**[[Static vs. Animated Visual Representations for Science Learning (Kaye, Small, Butcher, & Chi)]]
*[[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]]
**[[Training Geometry Concepts with Visual and Verbal Sources (Burchfield, Aleven, & Butcher)]]
*[[Visual Representations in Science Learning | Visual Representations in Science Learning (Davenport, Klahr & Koedinger)]]
* Cotraining in language learning
**[[Co-training of Chinese characters| Co-training of Chinese characters (Liu, Perfetti, Dunlap, Zi, Mitchell)]]
**[[Co-training and pairing| The pairing effect in Chinese cotraining (Liu, Perfetti, Dunlap, Wu, Mitchell)]]
*[[Learning Chinese pronunciation from a “talking head”| Learning Chinese pronunciation from a “talking head” (Liu, Massaro, Dunlap, Wu, Chen,Chan, Perfetti)]] [Was in Refinement and Fluency]
*[[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]]
*[[Learning About Emergence and Heat Transfer (Chi)]]

=== Examples and Explanations ===
*[[Booth | Improving skill at solving equations through better encoding of algebraic concepts (Booth, Siegler, Koedinger & Rittle-Johnson)]]
*[[McLaren_et_al_-_Studying_the_Learning_Effect_of_Personalization_and_Worked_Examples_in_the_Solving_of_Stoich_Problems | Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren, Koedinger & Yaron)]]
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]]
**[[Note-Taking: Restriction and Selection]] (completed)
**[[Note-Taking: Coordination]] (planned)
*[[REAP_main | The REAP Project: Implicit and explicit instruction on word meanings (Juffs & Eskenazi)]]
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger)
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed)
**[[Effect of adding simple worked examples to problem-solving in algebra learning]] (completed, analysis in progress)
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)
*[[Bridging_Principles_and_Examples_through_Analogy_and_Explanation | Bridging Principles and Examples through Analogy and Explanation (Nokes & VanLehn)]]
*[[Does learning from worked-out examples improve tutored problem solving? | Does learning from worked-out examples improve tutored problem solving? (Renkl, Aleven & Salden)]]
*[[Ringenberg_Examples-as-Help | Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn)]]
*[[Roll_IPL | Invention as Preparation for Learning (Roll, Aleven, Koedinger & Schwartz)]]
*[[Baker_Choices_in_LE_Space | How Content and Interface Features Influence Student Choices Within the Learning Space (Baker, Corbett, Koedinger, & Rodrigo)]]
*[[Mayer_and_McLaren_-_Social_Intelligence_And_Computer_Tutors | Building Social Intelligence into Computer-Based Tutors (Mayer & McLaren)]]

== Annotated Bibliography ==
Much research in human and machine learning research has advocated various kinds of “multiples” to assist learning:
* multiple data sources (e.g., human learning (HL): Mayer, 2001; machine learning (ML): Blum & Mitchell, 1998; Collins & Singer, 1999).
* multiple representations (e.g., HL: Ainsworth & Van Labeke, 2004; ML: Liere & Tadepalli, 1997),
* multiple strategies (e.g., HL: Klahr & Siegler, 1978; ML: Michalski & Tecucci 1997; Saitta, Botta, & Neri, 1993);
* multiple learning tasks (e.g., HL: Holland, Holyoak, Nisbett, & Thagard, 1986; ML: Caruana, 1997; Case, Jain, Ott, Sharma, & Stephan, 1998);

Experiments in human learning have demonstrated, for instance, that instruction that combines rules or principles and [[example]]s yields better results than either alone (Holland, Holyoak, Nisbett, & Thagard, 1986) or, for instance, iterative instruction of both [[Procedural tasks|procedures]] and [[Conceptual tasks|concepts]] better learning (Rittle-Johnson & Koedinger, 2002; Rittle-Johnson, Siegler, & Alibali, 2001).

Experiments in machine learning have demonstrated how more robust, generalizable learning can be achieved by training a single learner on ''multiple'' related tasks (Caruana 1997) or by training ''multiple'' learning systems on the same task (Blum & Mitchell 1998; Collins & Singer 1999; Muslea, Minton, & Knoblock, 2002). Blum and Mitchell (1998) provide both empirical results and a proof of the circumstances under which strategy combinations enhance learning. In particular, the [[co-training]] approach for combining multiple learning strategies yields better learning to the extent that the learning strategies produce “uncorrelated errors” – when one is wrong the other is often right. As an example of PSLC work, Donmez et al. (2005) demonstrate, using a multi-dimensional collaborative process analysis, that regularities across ''multiple'' codings of the same data can be exploited for the purpose of improving text classification accuracy for difficult codings.

An ambitious goal of PSLC is provide a rigorous causal theory of human learning results at the level of precision of machine learning research.

* Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. The Journal of the Learning Sciences, 11(1), 25–61.
* Ainsworth, S.E. & Van Labeke (2004) Multiple forms of dynamic representation. Learning and Instruction, 14(3), 241-255.
* Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 92–100). New York: ACM Press. Available: citeseer.nj.nec.com/blum98combining.html
* Caruana, R. (1997). Multitask learning. Machine Learning 28(1), 41-75. Available: citeseer.nj.nec.com/caruana97multitask.html.
* Case, J., Jain, S., Ott, M., Sharma, A., & Stephan, F. (1998). Robust learning aided by context. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 44-55). New York: ACM Press.
* Collins, M., & Singer, Y. (1999). Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (pp. 189–196).
* Donmez, P., Rose, C. P., Stegmann, K., Weinberger, A., and Fischer, F. (2005). Supporting CSCL with Automatic Corpus Analysis Technology, to appear in the Proceedings of Computer Supported Collaborative Learning.
* Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
* Klahr D., and Siegler R.S. (1978). The Representation of Children's Knowledge. In H.W. Reese and L.P. Lipsitt (Eds.), Advances in Child Development and Behavior, Academic Press, New York, NY, pp. 61-116.
* Liere, R., & Tadepalli, P. (1997). Active learning with committees for text categorization. In Proceedings of AAAI-97, 14th Conference of the American Association for Artificial Intelligence (pp. 591—596). Menlo Park, CA: AAAI Press.
* Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.
* Michalski, R., & Tecuci, G. (Eds.) (1997). Machine learning: A multi-strategy approach. Morgan Kaufmann.
* Muslea, I., Minton, S., & Knoblock, C. (2002). Active + semi-supervised learning = robust multi-view learning. In Proceedings of ICML-2002. Sydney, Australia.
* Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–262.
* Rittle-Johnson, B., & Koedinger, K. R. (2002). Comparing instructional strategies for integrating conceptual and procedural knowledge. Paper presented at the Psychology of Mathematics Education, National, Athens, GA.
* Saitta, L., Botta, M., & Neri, F. (1993). Multi-strategy learning and theory revision. Machine Learning, 11(2/3), 153–172.
[[Category:Cluster]]

Learning the role of radicals in reading Chinese

2007-12-18T01:54:40Z

Vanlehn: /* Findings */

----
Summary Table
*Node Title: Semantic Radicals Study
*Researchers: Susan Dunlap, Ying Liu, Charles Perfetti, Sue-mei Wu
*PIs: Charles Perfetti, Ying Liu, Min Wang
*Others who have contributed 160 hours or more:
*Graduate Students: Susan Dunlap
*Study Start Date Sep 1, 2005
*Study End Date Dec 31, 2006
*LearnLab Site and Courses , CMU Chinese Online
*Number of Students: 20
*Total Participant Hours for the study: 60
*Data in the Data Shop: in progress
----

== Abstract ==

Does providing reliable semantic information help second language learners acquire new words? Two experiments investigated whether adult learners of Chinese benefited from explicit instruction of semantic information when learning new characters. We manipulated whether semantic information was a reliable cue to word meaning and whether predictability was taught [[explicit instruction|explicitly]]. We measured learning outcomes with translation and semantic judgment tasks.

== Glossary ==

Semantic radical; [[Explicit instruction]]; [[Implicit instruction]]; [[Cue validity]]

== Research Question ==

Does providing reliable semantic information help second language learners acquire new words?

== Background ==
A '''background''' and significance section that briefly summarizes prior work on the research question and why it is important to answer it

Previous research has shown that non-native learners of Chinese do not discern the presence of [[cue validity|helpful cues]] in the orthography unless such relationships are taught explicitly (Taft & Chung, 1999). But because semantic cues in Chinese are not always reliable predictors of word meaning (Hanley, 2005; Shu, Chen, Anderson, Wu, & Xuan, 2003), it may actually be more confusing for a beginning learner to be taught these relationships. The aim of this study was to determine how [[reliability]] of cues can affect learning. As in every language, Chinese has rules and exceptions to those rules. The written form of Chinese contains a high percentage of compound characters, which are single, one-syllable words made up of semantic and phonetic radicals. These radicals, or linguistic subcomponents, often provide cues to the character’s meaning and pronunciation. However, a reader cannot rely solely on using this strategy to decode new words in Chinese. Therefore, we wanted to ascertain whether it is helpful to teach the sometimes ambiguous relationship between linguistic subcomponents and whole word definitions.

== Dependent variables ==
The '''dependent variables''', which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome

[[Normal post-test]] measures:
- accuracy and response time on a semantic category judgment task with previously learned items (Experiment 1)

- accuracy of translating previously learned Chinese characters into English (Experiment 2)

[[Transfer]] measure:
- accuracy on a multiple-choice translation task with new Characters (Experiments 1 and 2)

== Independent variables ==
The '''independent variables''', which are typically include instructional environment, activity or method, and perhaps some student characteristics, such as gender or first language

Training condition was either explicit (information was provided about the semantic radical’s meaning in relation to meaning of the character) or implicit (no additional information was provided). Being explicit about the radical is an instance of [[feature focusing]] [[instructional method]]. Each semantic radical was either reliable (its meaning was associated with the meaning of the characters) or unreliable (its meaning was unrelated to the meaning of the character in which it appeared).

== Hypothesis
The '''hypothesis''', which is a concise statement of the relationship among the variables that answers the research question

We predict an interaction between [[reliability]] and [[explicit instruction|explicitness]], such that learners will perform better on items studied in the explicit condition compared to the implicit condition, and this effect will be greater for characters with reliable semantic radicals than characters with unreliable semantic radicals.

== Findings ==
Preliminary analyses show that providing semantic cues promoted retention of target characters and aided in transferring knowledge to new characters. Reliability of cues had no additional effect on retention or transfer.

== Explanation ==
An '''explanation''', which is short (a paragraph or two) and typically mentions unobservable, hypothetical attributes of the students (e.g., the students’ knowledge or motivation) and cognitive or social processes that affect them

We theorize that learners benefit from being taught the connection between semantic subcomponents of words and the meanings of words, and they adopt this strategy in learning new vocabulary.

== Descendents ==
The '''descendents''', which lists links to descendent nodes of this one, if there are any

None yet.

== Further information ==
A '''further information''' section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.)

None yet.

Refinement and Fluency

2007-12-18T01:49:03Z

Vanlehn: /* Knowledge accessibility */

= The PSLC Refinement and Fluency cluster =

== Abstract ==
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.

1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components.

2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.

3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.

4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.

5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns.

6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.

7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)

8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.

9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies.

10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.

The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.

This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.

 <center>[[Image:rf-theory.jpg]]</center>

==Significance==
A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.

== Glossary ==
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.

== Research question ==
The overall research question is how can instruction optimally organize the presentation of complex targeted knowledge, taking into account the learner’s existing knowledge as well as an analysis of the target domain? In examining this general question, the studies focus on the following dimensions of instructional organization, among others: the cognitive demands of knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.

== Independent variables ==
At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.

== Dependent variables ==
The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.

== Hypotheses ==
The overall hypothesis is that instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. A corollary of this hypothesis is that learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task.

Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:

1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.

2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)

3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.

4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.

5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.

6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].

7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.

8. Learning to learn hypothesis: The acquisition of skills such as analysis, help-seeking, or advance organizers can promote future learning.

9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.

== Explanation ==
All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.

== Descendents ==

=== Explicit instruction ===
'''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form
* [[Learning the role of radicals in reading Chinese]] (Liu et al.)
* [[Basic skills training|French dictation training]] (MacWhinney)
* [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)

'''B. Explicit attention manipulations''' studies typically vary features available to learner
* [[Chinese pinyin dictation]] (Zhang-MacWhinney)
* [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]
* [[Learning French gender cues with prototypes]] (Presson, MacWhinney)

'''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]
* [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)
* [[Japanese fluency]] (Yoshimura-MacWhinney)
* [[Fostering fluency in second language learning]] (De Jong, Perfetti)
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)

=== Knowledge accessibility ===
'''A. Background knowledge''' These projects directly study effects of learners' background knowledge
* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in Interactive Communication]
* [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)

'''B. Availability of knowledge during learning'''
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]
* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]
* [[Arithmetical fluency project]] (Fiez)
* [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, & Liu)

=== Active processing ===
These projects also include some addressing issues of learner control
* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) [Also in Coordinative Learning]
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]] [Also in Coordinative Learning]
**[[Note-Taking: Restriction and Selection]] (completed)
**[[Note-Taking: Focusing On Concepts]] (planned)
**[[Note-Taking: Focusing On Quantity]] (planned)
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger)
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress) [Also in Coordinative Learning]

===Other===

* [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)

== Annotated bibliography ==
Forthcoming

[[Category:Cluster]]

Instructional Principles and Hypotheses

2007-12-12T04:50:01Z

Vanlehn: /* List of Instructional Principles and Hypotheses */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===Generalization hierarchy of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.) See also [[Learning event scheduling]] and [[Instructional schedule]]

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[deep-level question]]s
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instruction principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Instructional Principles and Hypotheses

2007-12-12T04:49:18Z

Vanlehn:

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.) See also [[Learning event scheduling]] and [[Instructional schedule]]

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[deep-level question]]s
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instruction principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Optimized scheduling

2007-12-12T04:45:52Z

Vanlehn:

[[Category:Glossary]]
[[Category:Refinement and Fluency]]
[[Category:Independent Variables]]
[[Category:Instructional Principle]]
[[Category:PSLC General]]

==Brief statement of principle==
Applying an [[instructional schedule]] that has been ordered to maximize [[robust learning]]. Mathematical models may often be used to produce optimized schedules by computing the knowledge component that will be most efficiently learned if practiced next.
==Description of principle==
===Operational definition===
Scheduling practice to maximize some future measure of learning given a fixed time of current practice.

===Examples===
Examples of optimized scheduling include [[learning event scheduling]] (see [[Optimizing the practice schedule|Pavlik's research program]]), the [[knowledge tracing]] algorithm used in [[Cognitive Tutors]] (see [[Cen's study]]), and adaptive [[fading]] of [[scaffolding]] or [[assistance]] (see [[Does learning from worked-out examples improve tutored problem solving? |Renkl's study]]).

==Experimental support==
See references.
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
Optimized scheduling is often a method for providing optimal [[repetition]]. The optimized scheduling of Pavlik (2005; 2007) balances the speed (reduced time cost of practice) advantage of [[recency]] with the long-term learning advantage of [[spaced practice]]. This speed advantage typically occurs for [[drill]] practice because more recent drill practice has fewer failures and therefore less need for costly review practice.

==Conditions of application==
A limiting condition of using the optimized scheduling of Pavlik is that it relies on a recency advantage for practice. Without this recency advantage, it is often true that maximal spacing is optimal as suggested in the practice guidelines. [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"]

For example, if each practice trial has a fixed duration this will result in no recency advanatge and maximal (or very wide) spacing will be optimal. However, many procedures uses the test trials since the [[testing effect]] has shown that tests result in stronger learning tha passive study. Tests often have a strong advantage when they occur with greater recency since this recency reduces the need for review in the case of failure.

==Caveats, limitations, open issues, or dissenting views==

==Variations (descendants)==
==Generalizations (ascendants)==
==References==
* Pavlik Jr., P. I. (2005). The microeconomics of learning: Optimizing paired-associate memory. Dissertation Abstracts International: Section B: The Sciences and Engineering, 66(10-B), 5704.
* Pavlik Jr., P. I. (2007). Timing is an order: Modeling order effects in the learning of information. In F. E., Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.), In order to learn: How order effects in machine learning illuminate human learning (pp. 137-150). New York: Oxford University Press.
* Pavlik Jr., P. I., Presson, N., Dozzi, G., Wu, S.-m., MacWhinney, B., & Koedinger, K. R. (2007). The FaCT (Fact and Concept Training) System: A new tool linking cognitive science with educators. In D. McNamara & G. Trafton (Eds.), Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society (pp. 397-402). Mahwah, NJ: Lawrence Erlbaum.
* Pavlik Jr., P. I., Presson, N., & Koedinger, K. R. (2007). Optimizing knowledge component learning using a dynamic structural model of practice. In R. Lewis & T. Polk (Eds.), Proceedings of the Eighth International Conference of Cognitive Modeling. Ann Arbor: University of Michigan.
* Pashler, H., Zarow, G., & Triplett, B. (2003). Is temporal spacing of tests helpful even when it inflates error rates? Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(6), 1051-1057.

Instructional Principles and Hypotheses

2007-12-12T04:17:11Z

Vanlehn: /* Interactive Communication */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[deep-level question]]s
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instruction principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Deep/Reflection questions

2007-12-12T04:15:49Z

Vanlehn: New page: This category subsumes Reflection questions and deep-level question. The shared idea is that questions are added to instruction that can stand alone without them. The questions a...

This category subsumes [[Reflection questions]] and [[deep-level question]]. The shared idea is that questions are added to instruction that can stand alone without them. The questions are intended to increase the depth of learning.

[[Category:Glossary]]
[[Category:Independent Variables]]

Interactive Communication

2007-12-12T04:12:43Z

Vanlehn: /* Independent variables */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection questions]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly)]]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also relevant to Refinement & Fluency, Knowledge component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Interactive Communication

2007-12-12T04:12:26Z

Vanlehn: /* Independent variables */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep/Reflection question]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly)]]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also relevant to Refinement & Fluency, Knowledge component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Interactive Communication

2007-12-12T04:09:27Z

Vanlehn: /* Independent variables */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep-level question]] (need to fix the column label in the matrix)

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly)]]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also relevant to Refinement & Fluency, Knowledge component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Instructional Principles and Hypotheses

2007-12-12T04:07:25Z

Vanlehn: /* A (temporary!) note on editing instructional principles and hypotheses pages */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instruction principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Category:Instructional Principle

2007-12-12T04:02:07Z

Vanlehn: New page: An instructional principle is a general hypothesis about the relative effectiveness of different kinds of instruction. It often has the form "A is more effective than not-A" or "A is more...

An instructional principle is a general hypothesis about the relative effectiveness of different kinds of instruction. It often has the form "A is more effective than not-A" or "A is more effective than B." We often use "principle" and "hypothesis" interchangably, although we also tend to reserve "principle" for more general hypothesis, hypotheses supported by copious evidence, or hypothesis that are commonly believed.

Instructional Principles and Hypotheses

2007-12-12T03:55:22Z

Vanlehn: /* A (temporary!) note on editing instructional principles and hypotheses pages */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instruction principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, as do Mayer's study, then the instruction method a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Instructional Principles and Hypotheses

2007-12-12T03:48:28Z

Vanlehn: /* A (temporary!) note on editing instructional principles and hypotheses pages */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An [[:Category:Instructional Principle|instruction principle]] is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. Instructional methods are a kind of [[:Category:Independent Variables|independent variable]], so they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Interaction plateau

2007-12-12T03:42:30Z

Vanlehn:

==Brief statement of principle==
[[Step-based instruction]] is just as effective as [[natural tutoring]], and more effective than [[low-interaction instruction]].

==Description of principle==
We see a plateau when learning gains are graphed on the y-axis and degree of interactivity is graphed on the x-axis. The learning gains increase as the degree of interaction increases from [[low-interaction instruction]] to [[step-based instruction]], but then the curve is flat from [[step-based instruction]] to [[natural tutoring]].

===Operational definition===
The steps of a task are defined by convention or the instruction. Step-based instruction is insures that students attended to correct steps and that they are encourage to derive them. For instance, a tutoring system might provide a form to fill in, where each blank in the form is a step, and then provide immediate feedback and hints on each blank in order to insure that the student derives a correct step for the blank.

On the other hand, natural tutoring is more interactive. The prototype is face-to-face human tutoring, although some natural langauge computer tutoring systems count as natural tutors as well. The key attribute is that they can interact at any grain size with the student. For instance, if a human tutor is helping a student fill in the blanks in the aforementioned form, and the student appears confused by one blank, then the human tutor might elicit a directed line of reasoning (Evens & Michael, 2006) where each inference in a long series is elicited from the student and leads eventually to filling in the blank correctly. The interaction plateau makes the counter-intuitive claim that such natural tutoring is no more effective than step-based instruction.

The interaction plateau also claims that low interaction instruction is less effective than step-based instruction. Low interaction instruction is subclassified into

* read-only instruction, such as reading a textbook or watching a video, and
* low-interaction problem solving, such as doing problems with either no feedback at all or feedback on answer only.

===Examples===
Suppose one wanted to help students kearn while doing their physics. Which of the following would be more effective?

* When a student wants help, the student clicks on a "I need a tutor" button, and gets audio-only tutoring from a human tutor who can see the students' screen. The human tutor helps the student finish the problem, and may stay on the line to help with further homework problems.

* The student uses [http://www.andes.pitt.edu Andes], a step-based homework helper (VanLehn et al, 2005).

* The student solves the homework problem on paper and enters the answer into [http://www.webassign.net/ WebAssign]. It indicates whether the answer is correct. If it is incorrect, WebAssign may give a hint; the student can submit the incorrect answer or rework their solution and enter a new answer.

The interaction plateau predicts that the first two treatments will be equally effective, and they they will be more effective than the third treatment, ceterus paribus.

==Experimental support==

===In vivo experiment support===
Reif and Scott (1999) found an interaction plateau when they compared human tutoring, a computer tutor and low-interaction problem solving. All students in their experiment were in the same physics class; the experiment varied only the way that the students did their homework. One group of 15 students did their physics homework problems individually in a six-person room where “two tutors were kept quite busy providing individual help” (ibid, pg. 826). Another 15 students did their homework on a computer tutor that had them either solve a problem or study a solution. When solving a problem, students got immediate feedback and hints on each step. When studying a problem, they were shown steps and asked to determine which one(s) were incorrect. This forced them to derive the steps. Thus, this computer tutor counts as step-based instruction. The remaining 15 students merely did their homework as usual, relying on the textbook, their friends and the course TAs for help. The human tutors and the computer tutors produced learning gains that were not reliably different, and yet both were reliably larger than the low-interaction instruction provided by normal homework (d=1.31 for human tutoring; d=1.01 for step-based computer tutoring).

Although these results are consistent with the interaction plateau, there is a potential confound. The human tutors and the computer tutor taught an effective problem solving method (Heller & Reif, 1984) which may or may not have been mentioned in the textbook and lectures. If not, then the poor learning gains of the untutored students may be due to their lack of access to content (the problem solving strategy) that was available to the tutored student. This potential confound does not affect the level part of the plateau; only the steep part.

===Laboratory experiment support===

In a series of experiments, (VanLehn et al., 2007) taught students to reason out answers to conceptual physics questions such as: “As the earth orbits the Sun, the sun exerts a gravitational force on it. Does the earth also exert a force on the sun? Why or why not?” In all conditions of the experiment, students first studied a short textbook, then solved several training problems. For each problem, the students wrote an short essay-long answer, then were tutored on its flaws, then read a correct, well-written essay. Students were expected to apply a certain set of concept in their essays—these comprised the correct steps. The treatments differed in how they tutored students when the essay lacked a step or had an incorrect step. There were four experimental treatments: (1) Human tutors who communicated via a text-based interface with student; (2) Why2-Atlas and (3) Why2-AutoTutor, both of which were natural language computer tutors designed to approach human tutoring; and (4) a simple step-based computer tutor that “tutored” a missing or incorrect step by merely display text that explained what the correct step was. A control condition had students merely read passages from a textbook without answering conceptual questions. The first 3 treatments all count as natural tutoring, so according to the interaction plateau, they should all have the same learning gains as the simple step-based tutoring system. All four experimental conditions should score higher than the control condition, as it is classified as read-only studying of text. Figure ## shows the post-test scores, adjusted for pretest scores in an ANCOVA. The four experimental conditions are not reliably different, and they all were higher than the read-only studying condition by approximately d=1.0. Thus, the results of experiments 1 and 2 support the interaction plateau.

(VanLehn et al., 2007) were surprised that the four experimental conditions tied, so they did several more experiments. The experiments used different assessment methods (e.g., far transfer; retention), different students (pre-physics vs. post-physics courses) and different numbers of training problems. The interaction plateau was observed in all experiments except one. In that experiment, students who had not taken college physics were trained with materials that were designed for students who had taken college physics, and human tutoring was more effective than the simple step-based computer tutor. This makes sense; if the materials are too far over the students’ current level of competence, reading doesn’t suffice for comprehension, and yet a human tutor can help “translate” the content into novice terms. The last 2 experiments used a completely overhauled set of materials designed especially for students who had not taken college physics, and again found an interaction plateau.

In a series of experiments, (Evens & Michael, 2006) tutored medical students in cardiovascular physiology. All students were first taught the basics of the baroreceptor reflex which controls human blood pressure. They were then given a training problem wherein an artificial pacemaker malfunctions and students must fill out a spreadsheet whose rows denoted physiological variables (e.g., heart rate; the blood volume per stroke of the heart, etc.) and whose column denoted time periods. Each cell was filled with a +, - or 0 to indicate that the variable was increasing, decreasing or constant. Each such entry was a step. The authors first developed a step-based tutoring system, CIRCSIM, that presented a short text passage for each incorrectly entered step. They then developed a sophisticated natural language tutoring system, CIRCSIM-tutor, which replaced the text passages with human-like typed dialogue intended to remedy not just the step but the concepts behind the step as well. They also used a read-only studying condition with an experimenter-written text, and they included conditions with expert human tutors interacting in typed text with students. Figure ### summarizes the results from several experiments that used the same assessments and training problems but different treatments. The treatments that count as Natural Tutoring (the expert human tutors and CIRCSIM-tutor) tied with each other and with the step-based computer tutor (CIRCSIM). The only conditions were learning gains were significantly different were the read-only text studying treatments. This pattern is consistent with the interaction plateau.

So far, step-based instruction was conducted by a computer tutoring system. However, this is not the only way to get students to derive each correct step. (Chi, Roy, & Hausmann, in press) gave students a video of a problem being solved by a human tutor and a tutee working at a white board. Students had to solve the same problem as the one being solved in the video, and they could do so any way they wished. Other studies (VanLehn, 1998; VanLehn, Jones, & Chi, 1992) suggested that students use two main strategies for solving problems when they have access to an isomorphic solved problem: They either copy each step from the example, or they generate each step and check it against the example’s step. The checking strategy counts as step-based instruction, but the copying strategy does not since it is a fairly syntactic process. The Chi et al study did not control strategies, the experiment did use either pairs of students working together on the problems (with or without a video) versus students working alone on the problems (with or without a video). Presumably, the pairs are much less likely to use the copying strategy than the solos. Thus, the pairs+video treatment (where the checking strategy was probably common) can be counted as step-based instruction, whereas the individuals+video (where the copying strategy was probably common) can be ignored, as copying hardly counts as problem solving at all. Besides these two conditions, the other conditions in the experiment were (3) human tutoring, (4) pairs solving problems with the aid of a textbook but no video, and (5) individual students with a textbook but no video. The latter two treatments count as low-interaction problem solving, as the students had no way to tell if the steps they generated were correct. The results, shown in Figure ###, are consistent with the interaction plateau. In particular, human tutoring and the step-based instruction condition (pairs+video) had the same learning gains, and these gains were reliably larger than the other treatments.

To summarize, these four studies have all displayed an interaction plateau. Granted, none of the studies were designed to test the interaction plateau hypothesis, so classifications of their conditions into low-interaction instruction, step-based instruction and natural tutoring may seem a bit forced. Even ignoring the names of the classes, when the treatment conditions are ordered from least interactive to most interactive, all 4 studies produced plateaus.

==Theoretical rationale==
Good instructors will design steps that are sufficiently close together that most students can, by the end of their homework, derive every step. Perhaps the students struggle to bridge the steps when they are first learning a new topic, but with step-based instruction, most students eventually can do the hidden reasoning that must be done to correctly bridge from every step to the next. Natural tutoring provides no added value. However, low-interaction problem solving harms learning by making it too difficult to generate correct steps, and read-only studying invites an illustion of knowing (Glenberg, Wilkinson, & Epstein, 1982) less learning.
==Conditions of application==
The interaction plateau applies only to learning to solve complex, multi-step tasks. Single-step tasks or domains without well-defined tasks are excluded.

The interaction plateau applies only when all students are taught the same content using the same tasks.
==Caveats, limitations, open issues, or dissenting views==
Although studies of non-expert tutors showed only modest learning gains (Cohen, Kulik & Kulik, 1982), Bloom's (1984) expert tutors elicited very large (2-sigma) learning gains, which are larger than the gains usually found in step-based instruction. Corbett (2001) has argued that current computer tutors, when allowed to use mastery learning, also achieve a 2-sigma learning gain. Moreover, Bloom's tutors used a larger threshold for mastery than his comparison treatments, which could account for some of their benefits. Nonetheless, the assumption of the human tutor's omnipotence is so widely believed that there is likely to be at least some truth in it.

==Variations (descendants)==
==Generalizations (ascendants)==
==References==
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. ''Educational Researcher'', 13, 4-16.

Corbett, A. (2001). Cognitive computer tutors: Solving the two-sigma problem. In ''User Modeling: Proceedings of the Eighth International Conference'' (pp. 137-147).

Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (in press). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. ''Cognitive Science''.

Cohen, P. A., Kulik, J. A., & Kulik, C.-L. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. ''American Educational Research Journal'', 19(2), 237-248.

Evens, M., & Michael, J. (2006). ''One-on-one Tutoring By Humans and Machines''. Mahwah, NJ: Erlbaum.

Glenberg, A. M., Wilkinson, A. C., & Epstein, W. (1982). The illusion of knowing: Failure in the self-assessment of comprehension. ''Memory & Cognition'', 10(6), 597-602.

Heller, J. I., & Reif, F. (1984). Prescribing effective human problem-solving processes: Problem descriptions in physics. ''Cognition and Instruction'', 1(2), 177-216.

Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. ''American Journal of Physics'', 67(9), 819-831.

VanLehn, K. (1998). Analogy events: How examples are used during problem solving. ''Cognitive Science'', 22(3), 347-388.

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? ''Cognitive Science'', 31(1), 3-62.

VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. ''The Journal of the Learning Sciences'', 2(1), 1-59.

VanLehn, K., Lynch, C., Schultz, K., Shapiro, J. A., Shelby, R. H., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. ''International Journal of Artificial Intelligence and Education'', 15(3), 147-204.

[[Category:Glossary]]

Example-rule coordination

2007-12-12T03:40:47Z

Vanlehn:

==Brief statement of principle==
Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that includes the same examples and rules but does not help students combine them.

==Description of principle==
Example-rule coordination refers to a class of [[instructional method]]s that involve combining instructional [[example]]s with other forms of instruction including [[self-explanation]], problem-solving practice, [[analogical comparison]]. Coordination support may occur through explicit prompting for self-explanations, interleaving [[worked examples]] and problems, fading [[assistance]] from worked examples to problems.
===Operational definition===
===Examples===
Studies exploring various forms of example-rule coordination include: Butcher's [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|integrated hints]] in Geometry, Booth's [[Booth |corrective self-explanation]] in Algebra, McLaren's [[Stoichiometry_Study | worked example interleaving]] in Chemistry, Eskenazi's [[REAP_main |vocabulary example personalization]] in English, Ringenberg's [[Ringenberg_Examples-as-Help |example-based help]] in Physics, Anthony's [[Effect of adding simple worked examples to problem-solving in algebra learning |worked example interleaving]] in Algebra, Noke's [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation |analogical comparison of examples]] in Physics, Renkl's [[Does learning from worked-out examples improve tutored problem solving? |example fading]] in Geometry.

==Experimental support==
See the pages listed above the examples section for studies providing experimental support. See also the references below.
===Laboratory experiment support===
===In vivo experiment support===

==Theoretical rationale==
Combining examples and rules can enhance [[refinement]] toward better [[feature validity]] of [[knowledge components]]. That refinement may be supported or enhanced by various instructional methods and learning processes. By prompting students to engage in [[self-explanation]] of an instructional example, students are more likely to try to express the more general rules inherent in the example and thus focus on the deep, relevant features rather than shallow, perceptual features that are irrelevant to correct application of the target [[knowledge component]].

Another way combining instructional examples and rules may enhance [[refinement]] is through [[self-supervised learning]] processes similar to [[co-training]] whereby a learner may draw on complementary strengths and weaknesses of learning by induction from instructional examples versus learning by comprehension of instructional text or rules. More specifically, a learner may identify and eliminate errors in induction from an instructional example by noticing an inconsistency with his or her comprehension of a given verbal rule. Or, conversely, a learner may identify and eliminate errors in comprehension of a rule by noticing an inconsistency with his or her induction (or analogical reasoning) from an example.

==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
== Variations (descendants) ==

[[Worked example principle]]

An instructional principle page should be created for effects of prompting [[Self-explanation|self-explanation]]. Also, if warranted, instructional principle or hypothesis page might also be created for methods of structuring or supporting [[Analogical comparison|analogical comparison]].

==Generalizations (ascendants)==
[[Coordinative Learning]]

==References==
References that need to be added:
#worked example references (Sweller, Renkl, etc.)
#specific papers on studies that combine example and rule instruction by Nisbett, Holyoak etc.
#self-explanation references (Chi etc.)
#analogical comparison refs (Gentner, Nokes, etc.)

* Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 92–100). New York: ACM Press. Available: citeseer.nj.nec.com/blum98combining.html
* Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
* Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–262.
* Rittle-Johnson, B., & Koedinger, K. R. (2002). Comparing instructional strategies for integrating conceptual and procedural knowledge. Paper presented at the Psychology of Mathematics Education, National, Athens, GA.

[[Category:Glossary]]
[[Category:Independent Variables]]

Instructional Principles and Hypotheses

2007-12-12T03:39:16Z

Vanlehn: /* Interactive Communication */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An instructional principle wiki page will usually state a general hypothesis about how one instructional method is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. Instructional methods are a kind of independent variable, so they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Worked example principle

2007-12-12T03:38:06Z

Vanlehn: /* Generalizations (ascendants) */

== Brief statement of principle ==

In contrast to the traditional approach of giving a list homework (or seatwork) problems for students to solve, students learn more efficiently and more robustly when more frequent study of worked examples is interleaved with problem solving practice.

== Description of principle ==

"In courses that are teaching new tasks, learning time can be saved by replacing some practice problems with worked examples" (Clark & Mayer, 2004, p. 177). In addition, most studies comparing interleaved worked examples and problems with all problmes have also shown improved learning outcomes, including robust learning outcomes.

"It would be an unusual (not to mention incompetent) teacher who did not use worked examples.  Similarly, textbooks universally use worked examples to illustrate new concepts.  The suggestion being made here goes beyond this limited use of worked examples.  Rather than using them merely to demonstrate how to use amathematical or scientific rule, the proposal is that they should be used inlarge numbers as a form of practice.  In other words, instead of practicing by solving many problems (an activity engaged in by most conscientious students), it is proposed that many of these problems could profitably be replaced by worked examples." Sweller, J. (1999) p73

=== Operational definition ===

=== Examples ===

Imagine instead of giving students a typical homework or seatwork assignment involving 8 problems, you give them an assignment where every other problem comes with a complete worked out solution. The even numbered items would be usual problems, like the following algebra problem: Solve 12 + 2x = 15 for x

 

The odd numbered problems, come with solutions, like this: Below an example solution to the problem: “Solve 12 + 2x = 15 for x” Study each step in this solution, so that you can better solve the next problem on your own:

12+2x = 15 2x = 15-12 2x = 3 x = 3/2 x = 1.5

 Which approach, asking for solutions to all 8 problems or interleaving 4 examples with 4 problems, will be lead to better student learning? You might think that the 8 problems require more work or that students might ignore the examples and thus, the 8 problems would lead to more learning. But, much research has shown that students typically learn more deeply and more easily from the second approach, when examples are interleaved between problems.

Teachers often think so many examples “give it away” or that students will not pay attention to the example. But, by having problems in between students are motivated to pay more attention to the example so as to prepare for the next problem or to resolve a question from the past problem. The problems break a students’ “illusion of knowing” (see the meta-cognition recommendation) that might otherwise lead them to skim the example and believe it is obvious.

It is important that students spend time actively engaged in learning and in genuine problem solving and reasoning. However, an emphasis on “learn by doing” is sometimes taken too far and students end up with homework problems or projects that are beyond their means. In such cases, they may spend much unproductive study time struggling without success. This time is often not only wasted but may increase a students’ frustration with the subject-matter and lead to unjustified feelings of not being good at math or science particularly. In contrast, during example study, students can focus their attention on understanding the principles underlying the examples instead of simply on finishing the problem. In early learning, the thought that goes simply into trying to solve the problem seems to distract students from trying to understand the principles underlying the solution.

Notice that in the example above, explanations for each step are not provided. It is best when students provide these explanations themselves and, while more research is needed, providing explanations can sometimes distract students doing so themselves and in other cases seems to provide no additional enhancement in student learning.

In whole classroom situation a teacher might implement this recommendation by going back and forth between a classroom or small group discussion around an example solution followed by small groups or individuals solving a problem (just one!) on their own. Then back to example study, for instance, by having students present their solutions and having others attempt to explain the steps (see the self-explanation recommendation). Now back to a second problem.

By giving the students frequent opportunities to study examples in between problem solving, students can more easily and more deeply acquire the big ideas, key concepts, or key principles that we want them to learn. With greater understanding, students will do better on harder problems in the future that require them to transfer these key concepts beyond the problems just like those they have seen before. 

== Experimental support ==

 "There is a lot of evidence for the effectiveness of learning from worked examples.  As an example, in one study twelve geoametry problems were used.  In the conventional group the learners solved all twelve peoblems as practice.  In the worked examples group, the learners received eight problems already worked out to study and then four problems to solve as practice.  Students in the worked examples group spent significantly less time studying and scored higher on a test than did those in the conventional group.  Furthermore, the worked examples group scored higher not only on test problems similar to those used during practice but also on different types of problems requiring application of the principles taught (Paas, 1992).  The investigators conclude that "training with partly or completely worked-out problems leads to less effort-demanding and better transfer performance and is more time efficient" (p. 433).  In fact, in one study, the use of worked examples allowed learners to complete a three-year mathematics course in two years (Zhu and Simon, 1987).  Positive effects of worked examples have been reported in a variety of cources teaching well-defined problems, including algebra, geometry, statistics, and programming". Clark & Mayer, 2003(pp 179)

=== Laboratory experiment support ===

=== In vivo experiment support ===

== Theoretical rationale ==

(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)

 "Working memory has a limited capacity that becomes inefficient when having to retain even a few items.  If the only way to build job-relevant skills is to perform many practice exercises, working memory can become overloaded by the mental work required to complete these exercises.  However, if limited working memory resources could be used to study worked examples and build new knowledge from them, some of this labor- intensive effort could be bypassed.  Worked examples are more efficient for learning new tasks because they reduce the load in working memory, thereby allowing the learner to learn the steps in problem solving.  Sweller and his colleagues distinguished between the intrinsic load of instructional materials that result from the inherent complexity of the content itself and the extraneous load imposed by the instructional design (Sweller, 1999; Sweller, Van Merrienboer and Paas, 1998). Learners who are studying complex topics will have to deal with high intrinsic mental load, especially if it's new information.  However, good e-learning can help learners manage that lead by using effective instructional methods.  Replacing some assigned problems with worked examples reduces the extraneous load, freeing working memory to allocate resources to the learning process.  This recommendation applies primarily to courses for novice learners who are most susceptable to cognitive overload".   Clark & Mayer, 2003 (pp178-179)

Another line of rationale suggests that worked examples make students engage in more self-explanation than they do during problem solving.

One (of perhaps many) open questions is what motivates students to process examples more deeply, that is, to engage in "generative processing" (Mayer) or "germane load" (Van Merrienboer and Paas?). The importance of interleaving examples and problems may be primarily about motivating students to deeply process the examples. Such an explanation is different from the "knowledge compilation" explanation for interleaving articulated by Trafton & Reiser (1993).

== Conditions of application ==

''1. Interleave examples and problems''. Trafton & Reiser (1993) showed that examples and problems should be given in an alternating or interleaved order (Example, Problem, Example, Problem, ...) and not blocked (Example, Example, ..., Problem, Problem, ...).

''2. Switch to problems later in leanring. ''The "expertise-reversal effect" suggests that it is earlier in skill development when the Worked Example Principle will be applicable, whereas later in development have students just solve problems without interleaved examples may be better (Kalyuga, Chandler, Tuovinen, & Sweller, 2001).

''3. Including explanations in examples may not help''.  See the discussion of not providing explanations in the example above in the Examples section.  Renkl and colleagues have explored this issue contrasting whether explanations are present or not (ADD REFS to Renkl).  

''4. Indicate subgoals in the example''.  In constrast to null or negative effects of adding explanations to examples (i.e., statements that justify a step), indicating how the steps fit into a hierarchy of goals and subgoals (e.g., by labeling some steps as key subgoals) does appear to aid learning.  (ADD REFS to Catrambone).

''5. Separate example study from problem solving''.  Having the example present during problem solving may encourage shallow processing (i.e., copying and small edits without understanding) of the example and may yield not benefit.   While there is clear theoretical support for this condition of application, there does not seem to be more solid experimental evidence for it.  Preliminary results from [[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input|Anthony's PSLC study]] are consistent with the idea that the worked example effect is not found when examples are provided to students while they are asked to solve an analogous problem. 

''6. Tell students to study the example to prepare for upcoming problem solving''.  According to John Sweller (personal communication with Ken Koedinger), in his experiments, students were instructed at the beginning to study each example in preparation for upcoming problem solving.  The prompting is recommended as critical to give students motivation to attend to and study the example.  It is not clear whether there is any experimental support for this condition of application (i.e., comparing learning with this instruction vs. without).  [Need to add references, this may be described in Sweller's book, Sweller, 1999]

==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
== Generalizations (ascendants) ==

[[Example-rule coordination principle]]

== References ==

Clark, R. C., & Mayer, R. E. (2003). e-Learning and the Science of Instruction : Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Jossey-Bass.

Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93(3), 579–588.

Paas, F. (1992). Training strategies for attaining transfer of problem solving skill in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434.

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.

Sweller, J. (1999). Instructional design in technical areas.  Camberwell, Australia: ACER Press

Sweller, J., van Merrienboer, J.J.G., & Paas, F. (1998).  Cognitive architectureand instructional design.  Educational Psychology Review, 10, 251-296

Trafton, J. G., & Reiser, B. J. (1993). The contribution of studying examples and solving problems to skill acquisition. Proceedings of the 15th Annual Conference of the Cognitive Science Society (pp. 1017–1022). Hillsdale: Lawrence Erlbaum Associates, Inc.

Zhu, X., & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166.

[[Category:Glossary]]
[[Category:Instructional Principle]]

Example-rule coordination principle

2007-12-12T03:37:17Z

Vanlehn: /* Variations (descendants) */

==Brief statement of principle==
Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that includes the same examples and rules but does not help students combine them.

==Description of principle==
Example-rule coordination refers to a class of [[instructional method]]s that involve combining instructional [[example]]s with other forms of instruction including [[self-explanation]], problem-solving practice, [[analogical comparison]]. Coordination support may occur through explicit prompting for self-explanations, interleaving [[worked examples]] and problems, fading [[assistance]] from worked examples to problems.
===Operational definition===
===Examples===
Studies exploring various forms of example-rule coordination include: Butcher's [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|integrated hints]] in Geometry, Booth's [[Booth |corrective self-explanation]] in Algebra, McLaren's [[Stoichiometry_Study | worked example interleaving]] in Chemistry, Eskenazi's [[REAP_main |vocabulary example personalization]] in English, Ringenberg's [[Ringenberg_Examples-as-Help |example-based help]] in Physics, Anthony's [[Effect of adding simple worked examples to problem-solving in algebra learning |worked example interleaving]] in Algebra, Noke's [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation |analogical comparison of examples]] in Physics, Renkl's [[Does learning from worked-out examples improve tutored problem solving? |example fading]] in Geometry.

==Experimental support==
See the pages listed above the examples section for studies providing experimental support. See also the references below.
===Laboratory experiment support===
===In vivo experiment support===

==Theoretical rationale==
Combining examples and rules can enhance [[refinement]] toward better [[feature validity]] of [[knowledge components]]. That refinement may be supported or enhanced by various instructional methods and learning processes. By prompting students to engage in [[self-explanation]] of an instructional example, students are more likely to try to express the more general rules inherent in the example and thus focus on the deep, relevant features rather than shallow, perceptual features that are irrelevant to correct application of the target [[knowledge component]].

Another way combining instructional examples and rules may enhance [[refinement]] is through [[self-supervised learning]] processes similar to [[co-training]] whereby a learner may draw on complementary strengths and weaknesses of learning by induction from instructional examples versus learning by comprehension of instructional text or rules. More specifically, a learner may identify and eliminate errors in induction from an instructional example by noticing an inconsistency with his or her comprehension of a given verbal rule. Or, conversely, a learner may identify and eliminate errors in comprehension of a rule by noticing an inconsistency with his or her induction (or analogical reasoning) from an example.

==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
== Variations (descendants) ==

* [[Worked example principle]]
* [[Prompted self-explanation hypothesis]]

If warranted, instructional principle or hypothesis page might also be created for methods of structuring or supporting [[Analogical comparison|analogical comparison]].

==Generalizations (ascendants)==
[[Coordinative Learning]]

==References==
References that need to be added:
#worked example references (Sweller, Renkl, etc.)
#specific papers on studies that combine example and rule instruction by Nisbett, Holyoak etc.
#self-explanation references (Chi etc.)
#analogical comparison refs (Gentner, Nokes, etc.)

* Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 92–100). New York: ACM Press. Available: citeseer.nj.nec.com/blum98combining.html
* Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
* Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–262.
* Rittle-Johnson, B., & Koedinger, K. R. (2002). Comparing instructional strategies for integrating conceptual and procedural knowledge. Paper presented at the Psychology of Mathematics Education, National, Athens, GA.

[[Category:Glossary]]
[[Category:Instructional Principle]]

Example-rule coordination principle

2007-12-12T03:35:57Z

Vanlehn: New page: ==Brief statement of principle== Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that ...

Instructional Principles and Hypotheses

2007-12-12T03:34:21Z

Vanlehn:

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Example-rule coordination principle]] (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]] See also independent variables [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted self-explanation hypothesis]] See also the independent variable [[Prompted Self-explanation]].

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]] - term in matrix (to the left) is not the same as glossary entry, [[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An instructional principle wiki page will usually state a general hypothesis about how one instructional method is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. Instructional methods are a kind of independent variable, so they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Prompted self-explanation hypothesis

2007-12-12T03:28:28Z

Vanlehn:

== Brief statement of principle ==
When students are given a [[worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.

== Description of principle ==
Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation|self-explanations]]. Some students have a natural tenancy to self-explain, while other students do little more than repeat the content of the example or expository text. The quality of the self-explanations themselves can be highly variable (Renkl, 1997). One instructional intervention that has been shown to be effective is to prompt students to self-explain (Chi et al., 1994). [[Prompting]] can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Koedinger & Aleven, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).

In the context of studying an example or reading a text, prompting for [[Self-explanation|self-explanations]] leads to greater learning gains than naturally occuring student practices.

=== Operational definition ===
* Self-explaining is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
* A meta-cognitive statement is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
* A self-explanation inference is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).
*Prompting is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.

=== Examples ===
Here are the instructions to self-explain, taken from Chi et al. (1994):

"We would like you to read each sentence out loud and then explain what it means to you. That is, what 
new information does each line provide for you, how does it relate to what you've already read, does it give 
you a new insight into your understanding of how the circulatory system works, or does it raise a question 
in your mind. Tell us whatever is going through your mind–even if it seems unimportant." 

These prompts were reworded to be used in Hausmann & VanLehn (2007):

* What new information does each step provide for you?
* How does it relate to what you've already seen?
* Does it give you a new insight into your understanding of how to solve the problems?
* Does it raise a question in your mind?

These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:

<Blockquote>A charged particle is in a region where there is an electric field E of magnitude 
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle 
is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote>

 

{| cellspacing="0" cellpadding="5" border="1"
|+ '''An example of prompting for self-explanining'''
|-
| style="border-bottom: 3px solid grey;" |
    Now that all the given information has been entered, we need to apply our knowledge of physics to solve the problem. 

    One way to start is to ask ourselves, “What quantity is the problem seeking?” In this case, the answer is the magnitude of the force on the particle due to the electric field. 

    We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative.

    We use the Force tool from the vector tool bar to draw the electric force. This brings up a dialog box. The force is on the particle and it is due to some unspecified source. We do know, however, that the type of force is electric, so we choose “electric” from the pull-down menu. For the orientation, we need to add 180 degrees to 22 degrees to get a force that is in a direction that is opposite of the direction of the electric field. Therefore we put 202 degrees. Finally, we use “Fe” to designate this as an electric force.

<center>[ PROMPT ]</center>

    Now that the direction of the electric force has been indicated, we can work on finding the magnitude. We must choose a principle that relates the magnitude of the electric force to the strength of the electric field, and the charge on the particle. The definition of an electric field is only equation that relates these three variables. We write this equation, in the equation window.

<center>[ PROMPT ]</center>

|}
Note. PROMPT = "Please begin your self-explanation."

== Experimental support ==

=== Laboratory experiment support ===
Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).

=== In vivo experiment support ===

Several in vivo experiments have leveraged laboratory work for inclusion of self-explaining in the classroom. Some in vivo experiments include:

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]
*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]
*[[Craig_questions|Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)]]

== Theoretical rationale ==

Prompting for self-explaining should increase the probability that a student engages in self-explaining, which includes an increase in the amount and accuracy of meta-cognitive monitoring statements and self-explanation inferences. Prompting for self-explaining is an attempt to increase the likelihood of traversing deep learning events.

{| border="1" cellpadding="5" cellspacing="0"
|-
|Start
# Process the line shallowly, e.g., paraphrasing it 
## There is nothing more to learn  Exit, with learning 
## The line is incomplete; its explanation is missing  Exit, with little learning 
# Try to process the line deeply, e.g., self-explain it 
## There is nothing missing from the line  Exit, with learning 
## The line is incomplete; its explanation is missing 
### The attempted self-explanation succeeds  Exit, with learning 
### The attempted self-explanation fails  Exit, with perhaps less learning 
|-
|}

== Conditions of application ==

When should a prompt for self-explanation be delivered? In many of the studies described on this page, prompts for self-explanation were offered after each step of a worked-out solution. The timing of the prompt may depend on the domain. For example, in Hausmann and VanLehn (2007), the domain was physics, which requires the acquisition of procedure knowledge. The prompt to self-explain was issued after each solution step. For a more conceptual domain, such as the circulatory system, the experimenter in Chi et al. (1994) prompted the students to self-explain after reading each page of a text on the circulatory system. Roughly one line (or idea) was contained on each page of the text. After several pages, the participants became accustomed to the procedure, and turning the page became an implicit prompt for the students to begin self-explaining (Chi, personal communication).

== Caveats, limitations, open issues, or dissenting views ==
Examples typically precede problem solving. For example, in Sweller and Cooper (1985; Experiment 2), they asked students to study 2 examples in preparation to solve 8 problems. Similarly, Chi et al. (1989) asked students to read through 4 chapters of a physics text, which contained several examples. After studying each chapter, the students were asked to solve problems related to the content that they just studied. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied 6 examples, then solved 6 problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until all problems and examples were completed.

The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.

== Variations (descendants) ==

== Generalizations (ascendants) ==

== References ==

Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explain with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179. [http://dx.doi.org/10.1016/S0364-0213%2802%2900061-7]

Bielaczyc, K., Pirolli, P., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221-252. [http://scholar.google.com/scholar?hl=en&client=firefox-a&rls=org.mozilla:en-US:official&hs=zUR&q=%22training+in+self-explanation+and+self-regulation+strategies:+Investigating+the+effects+of+knowledge+acquisition+activities+on+problem+solving%22&um=1&ie=UTF-8&sa=N&tab=ws]

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]

Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [http://www.pitt.edu/~bobhaus/hausmann2002.pdf]

Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]

McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222-233. [http://www.ingentaconnect.com/content/psocpubs/brm/2004/00000036/00000002/art00007]

Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [http://dx.doi.org/10.1016/S0364-0213(99)80017-2]

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89. [http://scholar.google.com/scholar?hl=en&lr=&client=firefox-a&cluster=16552570726007249431]

Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. [http://citeseer.ist.psu.edu/rd/40331946%2C149956%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/2910/http:zSzzSzwww.aic.nrl.navy.milzSz%7EtraftonzSzpaperszSzcogsci93-exp1.pdf/the-contributions-of-studying.pdf]

VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.

[[Category:Glossary]]
[[Category:Instructional Principle]]

Prompted Self-explanation

2007-12-12T03:25:28Z

Vanlehn:

Self-explaining is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
* A meta-cognitive statement is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
* A self-explanation inference is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).

Prompting is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.

=== Examples ===
Here are the instructions to self-explain, taken from Chi et al. (1994):

"We would like you to read each sentence out loud and then explain what it means to you. That is, what 
new information does each line provide for you, how does it relate to what you've already read, does it give 
you a new insight into your understanding of how the circulatory system works, or does it raise a question 
in your mind. Tell us whatever is going through your mind–even if it seems unimportant." 

These prompts were reworded to be used in Hausmann & VanLehn (2007):

* What new information does each step provide for you?
* How does it relate to what you've already seen?
* Does it give you a new insight into your understanding of how to solve the problems?
* Does it raise a question in your mind?

These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:

<Blockquote>A charged particle is in a region where there is an electric field E of magnitude 
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle 
is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote>

 

{| cellspacing="0" cellpadding="5" border="1"
|+ '''An example of prompting for self-explanining'''
|-
| style="border-bottom: 3px solid grey;" |
    Now that all the given information has been entered, we need to apply our knowledge of physics to solve the problem. 

    One way to start is to ask ourselves, “What quantity is the problem seeking?” In this case, the answer is the magnitude of the force on the particle due to the electric field. 

    We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative.

    We use the Force tool from the vector tool bar to draw the electric force. This brings up a dialog box. The force is on the particle and it is due to some unspecified source. We do know, however, that the type of force is electric, so we choose “electric” from the pull-down menu. For the orientation, we need to add 180 degrees to 22 degrees to get a force that is in a direction that is opposite of the direction of the electric field. Therefore we put 202 degrees. Finally, we use “Fe” to designate this as an electric force.

<center>[ PROMPT ]</center>

    Now that the direction of the electric force has been indicated, we can work on finding the magnitude. We must choose a principle that relates the magnitude of the electric force to the strength of the electric field, and the charge on the particle. The definition of an electric field is only equation that relates these three variables. We write this equation, in the equation window.

<center>[ PROMPT ]</center>

|}
Note. PROMPT = "Please begin your self-explanation."

== References ==
Chi, M. T. H. (2000). Self-explaining: The dual processes of generating and repairing mental models. In R. Glaser (Ed.), ''Advances in Instructional Psychology'' (pp. 161-238). Mahwah, NJ: Erlbaum.

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]

Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]

[[Category:Glossary]]
[[Category:Independent Variables]]

Prompted self-explanation hypothesis

2007-12-12T03:17:50Z

Vanlehn: New page: == Brief statement of principle == Many empirical studies have shown that there is a large amount of variance when it comes to individually produced self-explanations...

== Brief statement of principle ==

Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation|self-explanations]]. Some students have a natural tenancy to self-explain, while other students do little more than repeat the content of the example or expository text. The quality of the self-explanations themselves can be highly variable (Renkl, 1997). One instructional intervention that has been shown to be effective is to prompt students to self-explain (Chi et al., 1994). [[Prompting]] can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Koedinger & Aleven, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).

In the context of studying an example or reading a text, prompting for [[Self-explanation|self-explanations]] leads to greater learning gains than naturally occuring student practices.

== Description of principle ==

=== Operational definition ===
* Self-explaining is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
* A meta-cognitive statement is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
* A self-explanation inference is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).
*Prompting is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.

=== Examples ===
Here are the instructions to self-explain, taken from Chi et al. (1994):

"We would like you to read each sentence out loud and then explain what it means to you. That is, what 
new information does each line provide for you, how does it relate to what you've already read, does it give 
you a new insight into your understanding of how the circulatory system works, or does it raise a question 
in your mind. Tell us whatever is going through your mind–even if it seems unimportant." 

These prompts were reworded to be used in Hausmann & VanLehn (2007):

* What new information does each step provide for you?
* How does it relate to what you've already seen?
* Does it give you a new insight into your understanding of how to solve the problems?
* Does it raise a question in your mind?

These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:

<Blockquote>A charged particle is in a region where there is an electric field E of magnitude 
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle 
is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote>

 

{| cellspacing="0" cellpadding="5" border="1"
|+ '''An example of prompting for self-explanining'''
|-
| style="border-bottom: 3px solid grey;" |
    Now that all the given information has been entered, we need to apply our knowledge of physics to solve the problem. 

    One way to start is to ask ourselves, “What quantity is the problem seeking?” In this case, the answer is the magnitude of the force on the particle due to the electric field. 

    We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative.

    We use the Force tool from the vector tool bar to draw the electric force. This brings up a dialog box. The force is on the particle and it is due to some unspecified source. We do know, however, that the type of force is electric, so we choose “electric” from the pull-down menu. For the orientation, we need to add 180 degrees to 22 degrees to get a force that is in a direction that is opposite of the direction of the electric field. Therefore we put 202 degrees. Finally, we use “Fe” to designate this as an electric force.

<center>[ PROMPT ]</center>

    Now that the direction of the electric force has been indicated, we can work on finding the magnitude. We must choose a principle that relates the magnitude of the electric force to the strength of the electric field, and the charge on the particle. The definition of an electric field is only equation that relates these three variables. We write this equation, in the equation window.

<center>[ PROMPT ]</center>

|}
Note. PROMPT = "Please begin your self-explanation."

== Experimental support ==

=== Laboratory experiment support ===
Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).

=== In vivo experiment support ===

Several in vivo experiments have leveraged laboratory work for inclusion of self-explaining in the classroom. Some in vivo experiments include:

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]
*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]
*[[Craig_questions|Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)]]

== Theoretical rationale ==

Prompting for self-explaining should increase the probability that a student engages in self-explaining, which includes an increase in the amount and accuracy of meta-cognitive monitoring statements and self-explanation inferences. Prompting for self-explaining is an attempt to increase the likelihood of traversing deep learning events.

{| border="1" cellpadding="5" cellspacing="0"
|-
|Start
# Process the line shallowly, e.g., paraphrasing it 
## There is nothing more to learn  Exit, with learning 
## The line is incomplete; its explanation is missing  Exit, with little learning 
# Try to process the line deeply, e.g., self-explain it 
## There is nothing missing from the line  Exit, with learning 
## The line is incomplete; its explanation is missing 
### The attempted self-explanation succeeds  Exit, with learning 
### The attempted self-explanation fails  Exit, with perhaps less learning 
|-
|}

== Conditions of application ==

When should a prompt for self-explanation be delivered? In many of the studies described on this page, prompts for self-explanation were offered after each step of a worked-out solution. The timing of the prompt may depend on the domain. For example, in Hausmann and VanLehn (2007), the domain was physics, which requires the acquisition of procedure knowledge. The prompt to self-explain was issued after each solution step. For a more conceptual domain, such as the circulatory system, the experimenter in Chi et al. (1994) prompted the students to self-explain after reading each page of a text on the circulatory system. Roughly one line (or idea) was contained on each page of the text. After several pages, the participants became accustomed to the procedure, and turning the page became an implicit prompt for the students to begin self-explaining (Chi, personal communication).

== Caveats, limitations, open issues, or dissenting views ==
Examples typically precede problem solving. For example, in Sweller and Cooper (1985; Experiment 2), they asked students to study 2 examples in preparation to solve 8 problems. Similarly, Chi et al. (1989) asked students to read through 4 chapters of a physics text, which contained several examples. After studying each chapter, the students were asked to solve problems related to the content that they just studied. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied 6 examples, then solved 6 problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until all problems and examples were completed.

The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.

== Variations (descendants) ==

== Generalizations (ascendants) ==

== References ==

Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explain with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179. [http://dx.doi.org/10.1016/S0364-0213%2802%2900061-7]

Bielaczyc, K., Pirolli, P., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221-252. [http://scholar.google.com/scholar?hl=en&client=firefox-a&rls=org.mozilla:en-US:official&hs=zUR&q=%22training+in+self-explanation+and+self-regulation+strategies:+Investigating+the+effects+of+knowledge+acquisition+activities+on+problem+solving%22&um=1&ie=UTF-8&sa=N&tab=ws]

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]

Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [http://www.pitt.edu/~bobhaus/hausmann2002.pdf]

Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]

McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222-233. [http://www.ingentaconnect.com/content/psocpubs/brm/2004/00000036/00000002/art00007]

Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [http://dx.doi.org/10.1016/S0364-0213(99)80017-2]

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89. [http://scholar.google.com/scholar?hl=en&lr=&client=firefox-a&cluster=16552570726007249431]

Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. [http://citeseer.ist.psu.edu/rd/40331946%2C149956%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/2910/http:zSzzSzwww.aic.nrl.navy.milzSz%7EtraftonzSzpaperszSzcogsci93-exp1.pdf/the-contributions-of-studying.pdf]

VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.

[[Category:Glossary]]
[[Category:Instructional Principle]]

Instructional Principles and Hypotheses

2007-12-12T03:17:14Z

Vanlehn: /* List of Instructional Principles and Hypotheses */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Interaction plateau hypothesis]]
* [[Example-rule coordination principle]]
** [[Worked example principle]]
** [[Prompted self-explanation hypothesis]]

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Example-rule coordination]] - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]].  See also [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted Self-explanation]]
*** [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]] - term in matrix (to the left) is not the same as glossary entry, [[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An instructional principle wiki page will usually state a general hypothesis about how one instructional method is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. Instructional methods are a kind of independent variable, so they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Example-rule coordination

2007-12-12T03:07:12Z

Vanlehn:

Instructional Principles and Hypotheses

2007-12-12T03:01:07Z

Vanlehn: /* List of Instructional Principles and Hypotheses */

===Creating Instructional Principle and Hypothesis Pages===
The PSLC is starting to maintain a collection of instructional principle pages. Each instructional principle page should be structured with the following headers:

#Brief statement of principle
#Description of principle
##Operational definition
##Examples
#Experimental support
##Level of support (either low, medium, or high) (See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)
##Laboratory experiment support
##In vivo experiment support
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])
#Conditions of application
#Caveats, limitations, open issues, or dissenting views
#Variations (descendants)
#Generalizations (ascendants)
#References

If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it 1) at least has the structure above (even if all sections aren't filled in) and 2) fill in or edit sections so they are consistent with your views. A template you can copy is provided further below.

We want to keep the number of principles down, at least at the highest level if generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.

Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.

Please also add references to the literature outside of PSLC to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers your write. By doing so, you can help others (and others can help you) identify relevant research in the field.

===List of Instructional Principles and Hypotheses===
From [[:Category:Instructional Principle]]

* [[Interaction plateau]]
* [[Example-rule coordination]]
** [[Worked example principle]]
** [[Prompted Self-explanation]]

=== List of independent variables that could become principles ===
From [[:Category:Independent Variables]]

==== Cross-cutting all 3 clusters ====

* [[Tutoring feedback]]
** [[Peer tutoring]]
** [[Prompted Self-explanation]]

==== [[Coordinative Learning]] ====

* [[Example-rule coordination]] - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).)
** [[Worked example principle]].  See also [[Worked examples]] and [[Learning by worked-out examples]].
** [[Prompted Self-explanation]]
*** [[Corrective self-explanation]]
* [[Visual-verbal integration]]
** [[Contiguous Representation]]
* [[Feature focusing]]

====[[Interactive Communication]]====
*[[Collaboration]]
**[[Peer tutoring]]
*[[Collaboration scripts]]
*[[Collaboratively observe]]
**[[Vicarious learning]]
*[[Deep/Reflection questions]] - term in matrix (to the left) is not the same as glossary entry, [[Deep-level question]]. (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the "deep questioning" recommendation (written primarily by Art Graesser).)
**[[Reflection questions]]
***[[Post-practice reflection]]
**[[Knowledge Construction Dialogues]]
**[[Prompted Self-explanation]]
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).
***[[Jointly constructed explanation]] - also perhaps a learning process?
*[[Instructional explanation]]

====[[Refinement and Fluency]]====
*[[Error correction support]]
*[[Explicit instruction]]
*[[Fluency Pressure]]
*[[Feedback Timing]] in matrix, but not in glossary.
*[[Feature focusing]]
*[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]
*[[Optimized scheduling]] in glossary, but not listed as independent variable - (NOTE: See the recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the spacing recommendation (written primarily by Hal Pashler). However, recent work by Pavlik qualifies conclusions of Pashler because most (if not all) of the research referenced does not control for time on task.)
** See also [[Learning event scheduling]] and [[Instructional schedule]]

====Unclassified====
*[[Assistance]]
*[[Availability]]
*[[Fading]]
*[[Implicit instruction]]
*[[Instructional method]]
*[[Scaffolding]]

===Template===
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.

<pre>
==Brief statement of principle==
==Description of principle==
===Operational definition===
===Examples===
==Experimental support==
===Laboratory experiment support===
===In vivo experiment support===
==Theoretical rationale==
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
==Generalizations (ascendants)==
==References==
[[Category:Glossary]]
[[Category:Instructional Principle]]
</pre>

===A (temporary!) note on editing instructional principles and hypotheses pages===

An instructional principle wiki page will usually state a general hypothesis about how one instructional method is better than some other baseline or control method. For example, Mayer's multimedia principle states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. Instructional methods are a kind of independent variable, so they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Let's just start in and see how it turns out.

Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have:

* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.

* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above.

If some of the structure above does not exist, please create it.

===Learning Processes===

Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.

[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]

A potentially different list of learning processes can be found at [[:Category:Learning Processes]].

Explicit instruction

2007-12-12T02:58:57Z

Vanlehn: Undo revision 6263 by Koedinger (Talk)

Explicit instruction most generally includes any instruction where the content is given to the student in a verbatim fashion often with words.

[[Instructional explanation]]s are one specific type of explicit instruction.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Help Tutor]]
[[Category:PSLC General]]

Visual-verbal integration

2007-12-12T02:56:40Z

Vanlehn: Undo revision 6143 by Koedinger (Talk)

Visual-verbal integration: The process by which learners combine visual and verbal knowledge into a coherent, flexible knowledge representation.

For example, in geometry, students need to connect the conceptual definition of a geometry principle (e.g., a verbal understanding of "Vertical Angles") with the visual diagram features that correspond to the relevant verbal knowledge (e.g., the visual instantiation of "Vertical Angles" in a problem diagram).

See also [[integration]] and [[coordination]].

[[Category:Glossary]]

[[Category:Independent Variables]]

[[Category:Visual-Verbal Learning (Aleven & Butcher Project)]]

Tutoring feedback

2007-12-12T02:55:09Z

Vanlehn:

Tutoring feedback is an interactive [[instructional method]] that involves asking students to solve problems or engage in a some constructive activity while a tutor monitors student actions ([[step]]s) and indicates whether or not those actions are correct (i.e., gives feedback). Tutoring feedback provides a level of instructional [[assistance]] that lies between [[worked examples]] and untutored problem solving (e.g., typical homework). Like untutored problem solving, tutored problem solving provides less assistance than a worked example because the solution is not given (at least not initially). However, if a student makes errors, then they get more assistance from tutoring problem solving than untutored problem solving because of the availability of tutoring feedback.

Many of PSLC's LearnLab courses involve the use of computer tutors that provide tutoring feedback. Such interactive feedback is often not as readily available in more traditional courses and thus the as-is or [[ecological control group]] in PSLC studies may present a harder challenge or high base from which to improve instruction.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:PSLC General]]

Fluency Pressure

2007-12-12T02:50:33Z

Vanlehn:

Fluency Pressure is present when an instructional manipulation requires the learner to make responses at a rate that exceeds the learner's current rate. An example comes from de Jong's study of English second language learners, who were given increasingly shorter periods of time in which to produce a short "speech", an activity that, by hypothesis, will increase the fluency of production.
[[Category:Glossary]]
[[Category:Independent Variables]]

Fluency Pressure

2007-12-12T02:49:57Z

Vanlehn:

Worked example principle

2007-12-12T02:11:43Z

Vanlehn:

== Brief statement of principle ==

In contrast to the traditional approach of giving a list homework (or seatwork) problems for students to solve, students learn more efficiently and more robustly when more frequent study of worked examples is interleaved with problem solving practice.

== Description of principle ==

"In courses that are teaching new tasks, learning time can be saved by replacing some practice problems with worked examples" (Clark & Mayer, 2004, p. 177). In addition, most studies comparing interleaved worked examples and problems with all problmes have also shown improved learning outcomes, including robust learning outcomes.

"It would be an unusual (not to mention incompetent) teacher who did not use worked examples.  Similarly, textbooks universally use worked examples to illustrate new concepts.  The suggestion being made here goes beyond this limited use of worked examples.  Rather than using them merely to demonstrate how to use amathematical or scientific rule, the proposal is that they should be used inlarge numbers as a form of practice.  In other words, instead of practicing by solving many problems (an activity engaged in by most conscientious students), it is proposed that many of these problems could profitably be replaced by worked examples." Sweller, J. (1999) p73

=== Operational definition ===

=== Examples ===

Imagine instead of giving students a typical homework or seatwork assignment involving 8 problems, you give them an assignment where every other problem comes with a complete worked out solution. The even numbered items would be usual problems, like the following algebra problem: Solve 12 + 2x = 15 for x

 

The odd numbered problems, come with solutions, like this: Below an example solution to the problem: “Solve 12 + 2x = 15 for x” Study each step in this solution, so that you can better solve the next problem on your own:

12+2x = 15 2x = 15-12 2x = 3 x = 3/2 x = 1.5

 Which approach, asking for solutions to all 8 problems or interleaving 4 examples with 4 problems, will be lead to better student learning? You might think that the 8 problems require more work or that students might ignore the examples and thus, the 8 problems would lead to more learning. But, much research has shown that students typically learn more deeply and more easily from the second approach, when examples are interleaved between problems.

Teachers often think so many examples “give it away” or that students will not pay attention to the example. But, by having problems in between students are motivated to pay more attention to the example so as to prepare for the next problem or to resolve a question from the past problem. The problems break a students’ “illusion of knowing” (see the meta-cognition recommendation) that might otherwise lead them to skim the example and believe it is obvious.

It is important that students spend time actively engaged in learning and in genuine problem solving and reasoning. However, an emphasis on “learn by doing” is sometimes taken too far and students end up with homework problems or projects that are beyond their means. In such cases, they may spend much unproductive study time struggling without success. This time is often not only wasted but may increase a students’ frustration with the subject-matter and lead to unjustified feelings of not being good at math or science particularly. In contrast, during example study, students can focus their attention on understanding the principles underlying the examples instead of simply on finishing the problem. In early learning, the thought that goes simply into trying to solve the problem seems to distract students from trying to understand the principles underlying the solution.

Notice that in the example above, explanations for each step are not provided. It is best when students provide these explanations themselves and, while more research is needed, providing explanations can sometimes distract students doing so themselves and in other cases seems to provide no additional enhancement in student learning.

In whole classroom situation a teacher might implement this recommendation by going back and forth between a classroom or small group discussion around an example solution followed by small groups or individuals solving a problem (just one!) on their own. Then back to example study, for instance, by having students present their solutions and having others attempt to explain the steps (see the self-explanation recommendation). Now back to a second problem.

By giving the students frequent opportunities to study examples in between problem solving, students can more easily and more deeply acquire the big ideas, key concepts, or key principles that we want them to learn. With greater understanding, students will do better on harder problems in the future that require them to transfer these key concepts beyond the problems just like those they have seen before. 

== Experimental support ==

 "There is a lot of evidence for the effectiveness of learning from worked examples.  As an example, in one study twelve geoametry problems were used.  In the conventional group the learners solved all twelve peoblems as practice.  In the worked examples group, the learners received eight problems already worked out to study and then four problems to solve as practice.  Students in the worked examples group spent significantly less time studying and scored higher on a test than did those in the conventional group.  Furthermore, the worked examples group scored higher not only on test problems similar to those used during practice but also on different types of problems requiring application of the principles taught (Paas, 1992).  The investigators conclude that "training with partly or completely worked-out problems leads to less effort-demanding and better transfer performance and is more time efficient" (p. 433).  In fact, in one study, the use of worked examples allowed learners to complete a three-year mathematics course in two years (Zhu and Simon, 1987).  Positive effects of worked examples have been reported in a variety of cources teaching well-defined problems, including algebra, geometry, statistics, and programming". Clark & Mayer, 2003(pp 179)

=== Laboratory experiment support ===

=== In vivo experiment support ===

== Theoretical rationale ==

(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)

 "Working memory has a limited capacity that becomes inefficient when having to retain even a few items.  If the only way to build job-relevant skills is to perform many practice exercises, working memory can become overloaded by the mental work required to complete these exercises.  However, if limited working memory resources could be used to study worked examples and build new knowledge from them, some of this labor- intensive effort could be bypassed.  Worked examples are more efficient for learning new tasks because they reduce the load in working memory, thereby allowing the learner to learn the steps in problem solving.  Sweller and his colleagues distinguished between the intrinsic load of instructional materials that result from the inherent complexity of the content itself and the extraneous load imposed by the instructional design (Sweller, 1999; Sweller, Van Merrienboer and Paas, 1998). Learners who are studying complex topics will have to deal with high intrinsic mental load, especially if it's new information.  However, good e-learning can help learners manage that lead by using effective instructional methods.  Replacing some assigned problems with worked examples reduces the extraneous load, freeing working memory to allocate resources to the learning process.  This recommendation applies primarily to courses for novice learners who are most susceptable to cognitive overload".   Clark & Mayer, 2003 (pp178-179)

Another line of rationale suggests that worked examples make students engage in more self-explanation than they do during problem solving.

One (of perhaps many) open questions is what motivates students to process examples more deeply, that is, to engage in "generative processing" (Mayer) or "germane load" (Van Merrienboer and Paas?). The importance of interleaving examples and problems may be primarily about motivating students to deeply process the examples. Such an explanation is different from the "knowledge compilation" explanation for interleaving articulated by Trafton & Reiser (1993).

== Conditions of application ==

''1. Interleave examples and problems''. Trafton & Reiser (1993) showed that examples and problems should be given in an alternating or interleaved order (Example, Problem, Example, Problem, ...) and not blocked (Example, Example, ..., Problem, Problem, ...).

''2. Switch to problems later in leanring. ''The "expertise-reversal effect" suggests that it is earlier in skill development when the Worked Example Principle will be applicable, whereas later in development have students just solve problems without interleaved examples may be better (Kalyuga, Chandler, Tuovinen, & Sweller, 2001).

''3. Including explanations in examples may not help''.  See the discussion of not providing explanations in the example above in the Examples section.  Renkl and colleagues have explored this issue contrasting whether explanations are present or not (ADD REFS to Renkl).  

''4. Indicate subgoals in the example''.  In constrast to null or negative effects of adding explanations to examples (i.e., statements that justify a step), indicating how the steps fit into a hierarchy of goals and subgoals (e.g., by labeling some steps as key subgoals) does appear to aid learning.  (ADD REFS to Catrambone).

''5. Separate example study from problem solving''.  Having the example present during problem solving may encourage shallow processing (i.e., copying and small edits without understanding) of the example and may yield not benefit.   While there is clear theoretical support for this condition of application, there does not seem to be more solid experimental evidence for it.  Preliminary results from [[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input|Anthony's PSLC study]] are consistent with the idea that the worked example effect is not found when examples are provided to students while they are asked to solve an analogous problem. 

''6. Tell students to study the example to prepare for upcoming problem solving''.  According to John Sweller (personal communication with Ken Koedinger), in his experiments, students were instructed at the beginning to study each example in preparation for upcoming problem solving.  The prompting is recommended as critical to give students motivation to attend to and study the example.  It is not clear whether there is any experimental support for this condition of application (i.e., comparing learning with this instruction vs. without).  [Need to add references, this may be described in Sweller's book, Sweller, 1999]

==Caveats, limitations, open issues, or dissenting views==
==Variations (descendants)==
== Generalizations (ascendants) ==

[[Example-rule coordination]]

== References ==

Clark, R. C., & Mayer, R. E. (2003). e-Learning and the Science of Instruction : Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Jossey-Bass.

Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93(3), 579–588.

Paas, F. (1992). Training strategies for attaining transfer of problem solving skill in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434.

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.

Sweller, J. (1999). Instructional design in technical areas.  Camberwell, Australia: ACER Press

Sweller, J., van Merrienboer, J.J.G., & Paas, F. (1998).  Cognitive architectureand instructional design.  Educational Psychology Review, 10, 251-296

Trafton, J. G., & Reiser, B. J. (1993). The contribution of studying examples and solving problems to skill acquisition. Proceedings of the 15th Annual Conference of the Cognitive Science Society (pp. 1017–1022). Hillsdale: Lawrence Erlbaum Associates, Inc.

Zhu, X., & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166.

[[Category:Glossary]]
[[Category:Instructional Principle]]

Deep-level question

2007-12-12T02:00:07Z

Vanlehn:

Deep-level question: This is a method of instruction wherein questions are added to instruction that is otherwise able to function without them. The questions invite students to draw links between mechanisms, components or processes (Craig, et al. 2006; Gholson & Craig, 2006). This would encompass many of the long answer question categories of the Graesser & Person (1994) Taxonomy (e.g. causal antecedent, causal consequence, comparison, & interpretation) and the higher level categories of Bloom's taxonomy (Bloom, 1956). Comprehension monitoring questions, text-base questions (McNamara & Kintch, 1996), content-free self-explanation prompts (Chi et al., 2001), are not included.

Although deep-level questions often require students to type or otherwise express their answers ([[Post-practice reflection (Katz)|PSLC project example]]), some deep-level questions are rhetorical in that students are not able to enter answers([[Craig questions|PSLC project example]]).

When deep-level questions precede the original instruction, they are a kind of advance organizers (Ausubel, 1960). When they follow the original instruction, they are a kind of [[reflection questions]]. However, they can also be inserted in the midst of the original instruction.

When this independent variable is manipulated, the contrast is often with the original instruction, which is often the [[ecological control]], that did not include the deep-level questions.

=== References ===
* Ausubel, D.P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology, 51, 267-272.
* Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives. Handbook 1: Cognitive domain. New York: McKay.
* Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.
* Graesser, A. C., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104-137.
* Gholson, B. & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&Craig2006.pdf]
* McNamara, D. S., & Kintsch, W. (1996). Learning from text: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247-288.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Interactive Communication]]
[[Category:Craig questions]]

Vicarious learning

2007-12-12T01:58:43Z

Vanlehn:

Vicarious Learning, although originally coined by Bandura (1962) to refer to learning of behavior (e.g., aggression) form watching videos of that behavior, it is used here to refer to a [[instructional method]] that occurs when learners see and/or hear a learning situation (i.e., a observed learner in an instructional situation) for which they are not the addressees and do not interact with the observed learner nor the observed learner's instruction(Gholson & Craig, 2006; Rosenthal & Zimmerman, 1978). Although the learning situation is often presented as video recordings of human interactions or as cartoon-like recreations of learning situations (Bandura, 1986), the definition encompasses live vicarious learning, e.g., students watching another student at the front of the class interacting with the teacher.

When manipulated, this variable often involves a contrast with
* different kinds of learning situation being observed, e.g., a problem being solved by an instruction (e.g., Chi, Roy & Hausmann, in press; Craig et al. 2000; Craig, et al. 2006; Driscoll et al. 2003; [[Craig questions|PSLC project example]]) , or
* different kinds of dyadic instruction, e.g., being a tutee. (Chi, Roy & Hausmann, in press; Craig et al., 2004; [[Craig observing|PSLC project example]] )

"Learning by observing" is a somewhat broader term.

=== References ===
* Bandura, A. (1962). Social learning through imitation. In M. R. Jones (Ed.), Nebraska symposium of motivation (pp. 211-269). Lincoln: University of Nebraska Press.
* Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
* Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (in press). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. ''Cognitive Science.''
* Craig, S. D., Driscoll, D., & Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. ''Journal of Educational Multimedia and Hypermedia, 13'', 163-183. [http://andes3.lrdc.pitt.edu/~scraig/publications/Craigetal2004VL.pdf]
* Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.
* Craig, S., D., Gholson B., Ventura, M., Graesser, A. C., & the Tutoring Research Group. (2000). Overhearing dialogues and monologues in virtual tutoring sessions: effects on questioning and vicarious learning. International Journal of Artificial Intelligence in Education (Special Issue: Analyzing Educational Dialogue Interaction), 11, 242-253.
* Gholson, B. & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&Craig2006.pdf]
* Rosenthal, R. L., & Zimmerman, B. J. (1978). Social learning and cognition. New York: Academic Press.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Interactive Communication]]
[[Category:Craig questions]]

Interactive Communication

2007-12-12T01:57:27Z

Vanlehn: /* Independent variables */

= The PSLC Interactive Communication cluster =

== Abstract ==
The studies in the Interactive Communication deal primarily with learning environments where there are two interacting, communicating agents, one of which is the student. The other [[agent]] is typically a second student, a human tutor or a tutoring system. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. We are trying to find out why such instructional, dyadic, interactive communication is sometimes highly effective and sometimes less effective. Sometimes we study highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare whole forms of communciation. Our hypothesis is simply that interactive communication is effective if it guides students to attend to the right [[knowledge components]].

 <center>[[Image:ic-theory.jpg]]</center>

== Background and Significance ==
Although instructional dialogue has been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993), we are focusing on more tractable albeit still complex situations: ''dyadic'' instructional dialogues, namely dialogues between: (a) a human tutor and a human student, (b) two human students, or (c) A computer tutor and a human student. Moreover, the dialogue are task-oriented (Grosz & Sidner, 1986) in that the participants are working together on a task rather than simply conversing with no shared goals or with opposing goals.

Early studies focused on the structure of dyadic instructional dialogue (e.g., Fox, 1993; Graesser, Person & Magliano, 1995; MacArthur, Stasz, & Zmuidzinas, 1990). When later studies compared the learning that occurred during dialogue vs. less interactive instruction (e.g., VanLehn, Graesser et al., 2007; Katz, Connelly & Allbritton, 2003; Evens & Michael, 2006; Cohen, Kulik & Kulik, 1982), they found surprisingly mixed results. Only 60% of the studies showed that interactive communication caused larger learning gains than less interactive instruction.

The interactive communication cluster is undertaking the next step in this important line of research by investigating when different types of interactive communication are effective and why. Sometimes we compare highly constrained forms of communication in order to vary isolated aspects, and sometimes we compare constrained interactive communication to passive communication (e.g., reading).

== Glossary ==
See [[:Category:Interactive Communication|Interactive Communication Glossary]]

== Research question ==
What properties of interactive communication promote robust learning?

== Independent variables ==
The independent variables (also called Treatment Variables) of the IC cluster appear as column headers in the matrix above. They are listed here with links to their glossary entries.

* [[Collaboration]]

* [[Vicarious learning]]

* [[Collaboration scripts]]

* [[Deep-level question]]

* [[Instructional explanation]]

* [[Prompted Self-explanation]]

* [[Tutoring feedback]]

* [[Error correction support]]

== Dependent variables ==
Measures of normal and robust learning.

== Hypothesis ==
Our central hypothesis is just a special case of the [[Knowledge component hypothesis]]: interactive communication is effective if it guides students to attend to the right [[knowledge components]]. The key words here are “guide” and “attend” because they may oppose each other. A dialogue that strongly guides the student may also cause the student to disengage and thus not attend to the knowledge component even if the student’s dialogue partner mentions them. On the other hand, an unguided dialogue may increase the student’s engagement but may skirt around the right knowledge components. That is, the [[assistance dilemma]] surfaces as the degree of ''learner control'' (a term from the older educational literature) or ''student initiativ''e (a nearly synonymous term from the natural language dialogue literature).

== Explanation ==
If we view a short episode of interactive communication as a [[learning event space]], there could be three reasons why one treatment might be more effective than another:

(1) The learning event spaces might have different paths with different content. For instance, if one person contributes critical information that the other person lacks, then their joint learning event space has paths that are absent in the learning event space of the second person if that person were working solo. That is, the ''topology'' of one space might be better than the topology of the other.

(2) If the learning event spaces in the two conditions are the same, then the interactive communication treatment might cause the students to traverse different paths than the control students. That is, the ''path choices'' of one treatment might be better than the path choices of the other.

(3) If the learning event spaces are the same and the students take the same paths, they still might learn more in one condition than another because of the way that they traversed the path. For instance, having a partner observe the student as the student traverse a path might cause the student to be more attentive to details and to remember more. That is, the ''path effects'' might differ in the treatment vs. the control.

== Descendents ==

=== Collaboration ===
When and how does collaboration between peers can increase robust learning? Problem solving, example studying and many other activities can be done alone, in pairs, or in pairs with various kinds of assistance, such as collaboration scripts. From the standpoint of an individual learner, having a partner offers more [[assistance]] than working alone, and having a partner plus other scaffolding offer even more assistance. Thus, the [[Assistance Hypothesis]] predicts an interaction between various forms of peer collaboration and students' prior competence.

*[[Craig_observing|Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]

*[[Hausmann_Diss|The effects of elaborative dialog on problem solving and learning (Hausmann & Chi, 2005)]]

*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]

*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]

*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]

=== Questioning ===
When and how can asking the student questions increase the student's robust learning? What kinds of questions are best?

*[[Craig_questions|Deep-level questions during example studying (Craig & Chi)]]

*[[Reflective Dialogues (Katz)|Reflective Dialogues (Katz, Connelly & Treacy)]]

*[[Post-practice reflection (Katz)|Post-practice reflection (Katz & Connelly)]]

=== Tell vs. elicit ===
When a tutor knows that something needs to be said, she or he must decide whether to ''tell'' it to the tutee, try to ''elicit'' it from the tutee via a question or prompt, or just ''wait'' and hope that the tutee says it. Similarly, if a tutor knows that something needs to be done, the tutor can do it, elicit the action from the student or just wait. An instructional designer faces the same choices. For each thing that needs to be said or done in the instructional dialogue, should the tutor or the student be made responsible for it? For instance, should the tutoring system point out errors to the students or should the students detect their errors? In general, assistance is higher when the tutor does a portion of the instructional activity than when the student does it.

*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]

*[[Student_Uncertainty|Does Treating Student Uncertainty as a Learning Impasse Improve Learning in Spoken Dialogue Tutoring? (Forbes-Riley & Litman)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also relevant to Refinement & Fluency, Knowledge component analysis]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]] [Also relevant to Refinement & Fluency, Explicit instruction and manipulations of attention & discrimination]

*[[The self-correction of speech errors (McCormick, O’Neill & Siskin)]]

*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]

[[Category:Cluster]]

Interaction plateau

2007-12-11T21:45:40Z

Vanlehn: /* Examples */

Interaction plateau

2007-12-11T21:42:09Z

Vanlehn: /* References */

==Brief statement of principle==
[[Step-based instruction]] is just as effective as [[natural tutoring]], and more effective than [[low-interaction instruction]].

==Description of principle==
We see a plateau when learning gains are graphed on the y-axis and degree of interactivity is graphed on the x-axis. The learning gains increase as the degree of interaction increases from [[low-interaction instruction]] to [[step-based instruction]], but then the curve is flat from [[step-based instruction]] to [[natural tutoring]].

===Operational definition===
The steps of a task are defined by convention or the instruction. Step-based instruction is insures that students attended to correct steps and that they are encourage to derive them. For instance, a tutoring system might provide a form to fill in, where each blank in the form is a step, and then provide immediate feedback and hints on each blank in order to insure that the student derives a correct step for the blank.

On the other hand, natural tutoring is more interactive. The prototype is face-to-face human tutoring, although some natural langauge computer tutoring systems count as natural tutors as well. The key attribute is that they can interact at any grain size with the student. For instance, if a human tutor is helping a student fill in the blanks in the aforementioned form, and the student appears confused by one blank, then the human tutor might elicit a directed line of reasoning (Evens & Michael, 2006) where each inference in a long series is elicited from the student and leads eventually to filling in the blank correctly. The interaction plateau makes the counter-intuitive claim that such natural tutoring is no more effective than step-based instruction.

The interaction plateau also claims that low interaction instruction is less effective than step-based instruction. Low interaction instruction is subclassified into

* read-only instruction, such as reading a textbook or watching a video, and
* low-interaction problem solving, such as doing problems with either no feedback at all or feedback on answer only.

===Examples===
Suppose one wanted to help students kearn while doing their physics. Which of the following would be more effective?

* When a student wants help, the student clicks on a "I need a tutor" button, and gets audio-only tutoring from a human tutor who can see the students' screen. The human tutor helps the student finish the problem, and may stay on the line to help with further homework problems.

* The student uses Andes, a step-based homework helper (VanLehn et al, 2005).

* The student solves the homework problem on paper and enters the answer into WebAssign. It indicates whether the answer is correct. If it is incorrect, WebAssign may give a hint; the student can submit the incorrect answer or rework their solution and enter a new answer.

The interaction plateau predicts that the first two treatments will be equally effective, and they they will be more effective than the third treatment, ceterus paribus.

==Experimental support==

===In vivo experiment support===
Reif and Scott (1999) found an interaction plateau when they compared human tutoring, a computer tutor and low-interaction problem solving. All students in their experiment were in the same physics class; the experiment varied only the way that the students did their homework. One group of 15 students did their physics homework problems individually in a six-person room where “two tutors were kept quite busy providing individual help” (ibid, pg. 826). Another 15 students did their homework on a computer tutor that had them either solve a problem or study a solution. When solving a problem, students got immediate feedback and hints on each step. When studying a problem, they were shown steps and asked to determine which one(s) were incorrect. This forced them to derive the steps. Thus, this computer tutor counts as step-based instruction. The remaining 15 students merely did their homework as usual, relying on the textbook, their friends and the course TAs for help. The human tutors and the computer tutors produced learning gains that were not reliably different, and yet both were reliably larger than the low-interaction instruction provided by normal homework (d=1.31 for human tutoring; d=1.01 for step-based computer tutoring).

Although these results are consistent with the interaction plateau, there is a potential confound. The human tutors and the computer tutor taught an effective problem solving method (Heller & Reif, 1984) which may or may not have been mentioned in the textbook and lectures. If not, then the poor learning gains of the untutored students may be due to their lack of access to content (the problem solving strategy) that was available to the tutored student. This potential confound does not affect the level part of the plateau; only the steep part.

===Laboratory experiment support===

In a series of experiments, (VanLehn et al., 2007) taught students to reason out answers to conceptual physics questions such as: “As the earth orbits the Sun, the sun exerts a gravitational force on it. Does the earth also exert a force on the sun? Why or why not?” In all conditions of the experiment, students first studied a short textbook, then solved several training problems. For each problem, the students wrote an short essay-long answer, then were tutored on its flaws, then read a correct, well-written essay. Students were expected to apply a certain set of concept in their essays—these comprised the correct steps. The treatments differed in how they tutored students when the essay lacked a step or had an incorrect step. There were four experimental treatments: (1) Human tutors who communicated via a text-based interface with student; (2) Why2-Atlas and (3) Why2-AutoTutor, both of which were natural language computer tutors designed to approach human tutoring; and (4) a simple step-based computer tutor that “tutored” a missing or incorrect step by merely display text that explained what the correct step was. A control condition had students merely read passages from a textbook without answering conceptual questions. The first 3 treatments all count as natural tutoring, so according to the interaction plateau, they should all have the same learning gains as the simple step-based tutoring system. All four experimental conditions should score higher than the control condition, as it is classified as read-only studying of text. Figure ## shows the post-test scores, adjusted for pretest scores in an ANCOVA. The four experimental conditions are not reliably different, and they all were higher than the read-only studying condition by approximately d=1.0. Thus, the results of experiments 1 and 2 support the interaction plateau.

(VanLehn et al., 2007) were surprised that the four experimental conditions tied, so they did several more experiments. The experiments used different assessment methods (e.g., far transfer; retention), different students (pre-physics vs. post-physics courses) and different numbers of training problems. The interaction plateau was observed in all experiments except one. In that experiment, students who had not taken college physics were trained with materials that were designed for students who had taken college physics, and human tutoring was more effective than the simple step-based computer tutor. This makes sense; if the materials are too far over the students’ current level of competence, reading doesn’t suffice for comprehension, and yet a human tutor can help “translate” the content into novice terms. The last 2 experiments used a completely overhauled set of materials designed especially for students who had not taken college physics, and again found an interaction plateau.

In a series of experiments, (Evens & Michael, 2006) tutored medical students in cardiovascular physiology. All students were first taught the basics of the baroreceptor reflex which controls human blood pressure. They were then given a training problem wherein an artificial pacemaker malfunctions and students must fill out a spreadsheet whose rows denoted physiological variables (e.g., heart rate; the blood volume per stroke of the heart, etc.) and whose column denoted time periods. Each cell was filled with a +, - or 0 to indicate that the variable was increasing, decreasing or constant. Each such entry was a step. The authors first developed a step-based tutoring system, CIRCSIM, that presented a short text passage for each incorrectly entered step. They then developed a sophisticated natural language tutoring system, CIRCSIM-tutor, which replaced the text passages with human-like typed dialogue intended to remedy not just the step but the concepts behind the step as well. They also used a read-only studying condition with an experimenter-written text, and they included conditions with expert human tutors interacting in typed text with students. Figure ### summarizes the results from several experiments that used the same assessments and training problems but different treatments. The treatments that count as Natural Tutoring (the expert human tutors and CIRCSIM-tutor) tied with each other and with the step-based computer tutor (CIRCSIM). The only conditions were learning gains were significantly different were the read-only text studying treatments. This pattern is consistent with the interaction plateau.

So far, step-based instruction was conducted by a computer tutoring system. However, this is not the only way to get students to derive each correct step. (Chi, Roy, & Hausmann, in press) gave students a video of a problem being solved by a human tutor and a tutee working at a white board. Students had to solve the same problem as the one being solved in the video, and they could do so any way they wished. Other studies (VanLehn, 1998; VanLehn, Jones, & Chi, 1992) suggested that students use two main strategies for solving problems when they have access to an isomorphic solved problem: They either copy each step from the example, or they generate each step and check it against the example’s step. The checking strategy counts as step-based instruction, but the copying strategy does not since it is a fairly syntactic process. The Chi et al study did not control strategies, the experiment did use either pairs of students working together on the problems (with or without a video) versus students working alone on the problems (with or without a video). Presumably, the pairs are much less likely to use the copying strategy than the solos. Thus, the pairs+video treatment (where the checking strategy was probably common) can be counted as step-based instruction, whereas the individuals+video (where the copying strategy was probably common) can be ignored, as copying hardly counts as problem solving at all. Besides these two conditions, the other conditions in the experiment were (3) human tutoring, (4) pairs solving problems with the aid of a textbook but no video, and (5) individual students with a textbook but no video. The latter two treatments count as low-interaction problem solving, as the students had no way to tell if the steps they generated were correct. The results, shown in Figure ###, are consistent with the interaction plateau. In particular, human tutoring and the step-based instruction condition (pairs+video) had the same learning gains, and these gains were reliably larger than the other treatments.

To summarize, these four studies have all displayed an interaction plateau. Granted, none of the studies were designed to test the interaction plateau hypothesis, so classifications of their conditions into low-interaction instruction, step-based instruction and natural tutoring may seem a bit forced. Even ignoring the names of the classes, when the treatment conditions are ordered from least interactive to most interactive, all 4 studies produced plateaus.

==Theoretical rationale==
Good instructors will design steps that are sufficiently close together that most students can, by the end of their homework, derive every step. Perhaps the students struggle to bridge the steps when they are first learning a new topic, but with step-based instruction, most students eventually can do the hidden reasoning that must be done to correctly bridge from every step to the next. Natural tutoring provides no added value. However, low-interaction problem solving harms learning by making it too difficult to generate correct steps, and read-only studying invites an illustion of knowing (Glenberg, Wilkinson, & Epstein, 1982) less learning.
==Conditions of application==
The interaction plateau applies only to learning to solve complex, multi-step tasks. Single-step tasks or domains without well-defined tasks are excluded.

The interaction plateau applies only when all students are taught the same content using the same tasks.
==Caveats, limitations, open issues, or dissenting views==
Although studies of non-expert tutors showed only modest learning gains (Cohen, Kulik & Kulik, 1982), Bloom's (1984) expert tutors elicited very large (2-sigma) learning gains, which are larger than the gains usually found in step-based instruction. Corbett (2001) has argued that current computer tutors, when allowed to use mastery learning, also achieve a 2-sigma learning gain. Moreover, Bloom's tutors used a larger threshold for mastery than his comparison treatments, which could account for some of their benefits. Nonetheless, the assumption of the human tutor's omnipotence is so widely believed that there is likely to be at least some truth in it.

==Variations (descendants)==
==Generalizations (ascendants)==
==References==
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. ''Educational Researcher'', 13, 4-16.

Corbett, A. (2001). Cognitive computer tutors: Solving the two-sigma problem. In ''User Modeling: Proceedings of the Eighth International Conference'' (pp. 137-147).

Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (in press). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. ''Cognitive Science''.

Cohen, P. A., Kulik, J. A., & Kulik, C.-L. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. ''American Educational Research Journal'', 19(2), 237-248.

Evens, M., & Michael, J. (2006). ''One-on-one Tutoring By Humans and Machines''. Mahwah, NJ: Erlbaum.

Glenberg, A. M., Wilkinson, A. C., & Epstein, W. (1982). The illusion of knowing: Failure in the self-assessment of comprehension. ''Memory & Cognition'', 10(6), 597-602.

Heller, J. I., & Reif, F. (1984). Prescribing effective human problem-solving processes: Problem descriptions in physics. ''Cognition and Instruction'', 1(2), 177-216.

Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. ''American Journal of Physics'', 67(9), 819-831.

VanLehn, K. (1998). Analogy events: How examples are used during problem solving. ''Cognitive Science'', 22(3), 347-388.

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? ''Cognitive Science'', 31(1), 3-62.

VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. ''The Journal of the Learning Sciences'', 2(1), 1-59.

VanLehn, K., Lynch, C., Schultz, K., Shapiro, J. A., Shelby, R. H., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. ''International Journal of Artificial Intelligence and Education'', 15(3), 147-204.

[[Category:Glossary]]
[[Category:Instructional Principle]]
[[Category:PSLC General]]

Interaction plateau

2007-12-11T21:40:31Z

Vanlehn:

==Brief statement of principle==
[[Step-based instruction]] is just as effective as [[natural tutoring]], and more effective than [[low-interaction instruction]].

==Description of principle==
We see a plateau when learning gains are graphed on the y-axis and degree of interactivity is graphed on the x-axis. The learning gains increase as the degree of interaction increases from [[low-interaction instruction]] to [[step-based instruction]], but then the curve is flat from [[step-based instruction]] to [[natural tutoring]].

===Operational definition===
The steps of a task are defined by convention or the instruction. Step-based instruction is insures that students attended to correct steps and that they are encourage to derive them. For instance, a tutoring system might provide a form to fill in, where each blank in the form is a step, and then provide immediate feedback and hints on each blank in order to insure that the student derives a correct step for the blank.

On the other hand, natural tutoring is more interactive. The prototype is face-to-face human tutoring, although some natural langauge computer tutoring systems count as natural tutors as well. The key attribute is that they can interact at any grain size with the student. For instance, if a human tutor is helping a student fill in the blanks in the aforementioned form, and the student appears confused by one blank, then the human tutor might elicit a directed line of reasoning (Evens & Michael, 2006) where each inference in a long series is elicited from the student and leads eventually to filling in the blank correctly. The interaction plateau makes the counter-intuitive claim that such natural tutoring is no more effective than step-based instruction.

The interaction plateau also claims that low interaction instruction is less effective than step-based instruction. Low interaction instruction is subclassified into

* read-only instruction, such as reading a textbook or watching a video, and
* low-interaction problem solving, such as doing problems with either no feedback at all or feedback on answer only.

===Examples===
Suppose one wanted to help students kearn while doing their physics. Which of the following would be more effective?

* When a student wants help, the student clicks on a "I need a tutor" button, and gets audio-only tutoring from a human tutor who can see the students' screen. The human tutor helps the student finish the problem, and may stay on the line to help with further homework problems.

* The student uses Andes, a step-based homework helper (VanLehn et al, 2005).

* The student solves the homework problem on paper and enters the answer into WebAssign. It indicates whether the answer is correct. If it is incorrect, WebAssign may give a hint; the student can submit the incorrect answer or rework their solution and enter a new answer.

The interaction plateau predicts that the first two treatments will be equally effective, and they they will be more effective than the third treatment, ceterus paribus.

==Experimental support==

===In vivo experiment support===
Reif and Scott (1999) found an interaction plateau when they compared human tutoring, a computer tutor and low-interaction problem solving. All students in their experiment were in the same physics class; the experiment varied only the way that the students did their homework. One group of 15 students did their physics homework problems individually in a six-person room where “two tutors were kept quite busy providing individual help” (ibid, pg. 826). Another 15 students did their homework on a computer tutor that had them either solve a problem or study a solution. When solving a problem, students got immediate feedback and hints on each step. When studying a problem, they were shown steps and asked to determine which one(s) were incorrect. This forced them to derive the steps. Thus, this computer tutor counts as step-based instruction. The remaining 15 students merely did their homework as usual, relying on the textbook, their friends and the course TAs for help. The human tutors and the computer tutors produced learning gains that were not reliably different, and yet both were reliably larger than the low-interaction instruction provided by normal homework (d=1.31 for human tutoring; d=1.01 for step-based computer tutoring).

Although these results are consistent with the interaction plateau, there is a potential confound. The human tutors and the computer tutor taught an effective problem solving method (Heller & Reif, 1984) which may or may not have been mentioned in the textbook and lectures. If not, then the poor learning gains of the untutored students may be due to their lack of access to content (the problem solving strategy) that was available to the tutored student. This potential confound does not affect the level part of the plateau; only the steep part.

===Laboratory experiment support===

In a series of experiments, (VanLehn et al., 2007) taught students to reason out answers to conceptual physics questions such as: “As the earth orbits the Sun, the sun exerts a gravitational force on it. Does the earth also exert a force on the sun? Why or why not?” In all conditions of the experiment, students first studied a short textbook, then solved several training problems. For each problem, the students wrote an short essay-long answer, then were tutored on its flaws, then read a correct, well-written essay. Students were expected to apply a certain set of concept in their essays—these comprised the correct steps. The treatments differed in how they tutored students when the essay lacked a step or had an incorrect step. There were four experimental treatments: (1) Human tutors who communicated via a text-based interface with student; (2) Why2-Atlas and (3) Why2-AutoTutor, both of which were natural language computer tutors designed to approach human tutoring; and (4) a simple step-based computer tutor that “tutored” a missing or incorrect step by merely display text that explained what the correct step was. A control condition had students merely read passages from a textbook without answering conceptual questions. The first 3 treatments all count as natural tutoring, so according to the interaction plateau, they should all have the same learning gains as the simple step-based tutoring system. All four experimental conditions should score higher than the control condition, as it is classified as read-only studying of text. Figure ## shows the post-test scores, adjusted for pretest scores in an ANCOVA. The four experimental conditions are not reliably different, and they all were higher than the read-only studying condition by approximately d=1.0. Thus, the results of experiments 1 and 2 support the interaction plateau.

(VanLehn et al., 2007) were surprised that the four experimental conditions tied, so they did several more experiments. The experiments used different assessment methods (e.g., far transfer; retention), different students (pre-physics vs. post-physics courses) and different numbers of training problems. The interaction plateau was observed in all experiments except one. In that experiment, students who had not taken college physics were trained with materials that were designed for students who had taken college physics, and human tutoring was more effective than the simple step-based computer tutor. This makes sense; if the materials are too far over the students’ current level of competence, reading doesn’t suffice for comprehension, and yet a human tutor can help “translate” the content into novice terms. The last 2 experiments used a completely overhauled set of materials designed especially for students who had not taken college physics, and again found an interaction plateau.

In a series of experiments, (Evens & Michael, 2006) tutored medical students in cardiovascular physiology. All students were first taught the basics of the baroreceptor reflex which controls human blood pressure. They were then given a training problem wherein an artificial pacemaker malfunctions and students must fill out a spreadsheet whose rows denoted physiological variables (e.g., heart rate; the blood volume per stroke of the heart, etc.) and whose column denoted time periods. Each cell was filled with a +, - or 0 to indicate that the variable was increasing, decreasing or constant. Each such entry was a step. The authors first developed a step-based tutoring system, CIRCSIM, that presented a short text passage for each incorrectly entered step. They then developed a sophisticated natural language tutoring system, CIRCSIM-tutor, which replaced the text passages with human-like typed dialogue intended to remedy not just the step but the concepts behind the step as well. They also used a read-only studying condition with an experimenter-written text, and they included conditions with expert human tutors interacting in typed text with students. Figure ### summarizes the results from several experiments that used the same assessments and training problems but different treatments. The treatments that count as Natural Tutoring (the expert human tutors and CIRCSIM-tutor) tied with each other and with the step-based computer tutor (CIRCSIM). The only conditions were learning gains were significantly different were the read-only text studying treatments. This pattern is consistent with the interaction plateau.

So far, step-based instruction was conducted by a computer tutoring system. However, this is not the only way to get students to derive each correct step. (Chi, Roy, & Hausmann, in press) gave students a video of a problem being solved by a human tutor and a tutee working at a white board. Students had to solve the same problem as the one being solved in the video, and they could do so any way they wished. Other studies (VanLehn, 1998; VanLehn, Jones, & Chi, 1992) suggested that students use two main strategies for solving problems when they have access to an isomorphic solved problem: They either copy each step from the example, or they generate each step and check it against the example’s step. The checking strategy counts as step-based instruction, but the copying strategy does not since it is a fairly syntactic process. The Chi et al study did not control strategies, the experiment did use either pairs of students working together on the problems (with or without a video) versus students working alone on the problems (with or without a video). Presumably, the pairs are much less likely to use the copying strategy than the solos. Thus, the pairs+video treatment (where the checking strategy was probably common) can be counted as step-based instruction, whereas the individuals+video (where the copying strategy was probably common) can be ignored, as copying hardly counts as problem solving at all. Besides these two conditions, the other conditions in the experiment were (3) human tutoring, (4) pairs solving problems with the aid of a textbook but no video, and (5) individual students with a textbook but no video. The latter two treatments count as low-interaction problem solving, as the students had no way to tell if the steps they generated were correct. The results, shown in Figure ###, are consistent with the interaction plateau. In particular, human tutoring and the step-based instruction condition (pairs+video) had the same learning gains, and these gains were reliably larger than the other treatments.

To summarize, these four studies have all displayed an interaction plateau. Granted, none of the studies were designed to test the interaction plateau hypothesis, so classifications of their conditions into low-interaction instruction, step-based instruction and natural tutoring may seem a bit forced. Even ignoring the names of the classes, when the treatment conditions are ordered from least interactive to most interactive, all 4 studies produced plateaus.

==Theoretical rationale==
Good instructors will design steps that are sufficiently close together that most students can, by the end of their homework, derive every step. Perhaps the students struggle to bridge the steps when they are first learning a new topic, but with step-based instruction, most students eventually can do the hidden reasoning that must be done to correctly bridge from every step to the next. Natural tutoring provides no added value. However, low-interaction problem solving harms learning by making it too difficult to generate correct steps, and read-only studying invites an illustion of knowing (Glenberg, Wilkinson, & Epstein, 1982) less learning.
==Conditions of application==
The interaction plateau applies only to learning to solve complex, multi-step tasks. Single-step tasks or domains without well-defined tasks are excluded.

The interaction plateau applies only when all students are taught the same content using the same tasks.
==Caveats, limitations, open issues, or dissenting views==
Although studies of non-expert tutors showed only modest learning gains (Cohen, Kulik & Kulik, 1982), Bloom's (1984) expert tutors elicited very large (2-sigma) learning gains, which are larger than the gains usually found in step-based instruction. Corbett (2001) has argued that current computer tutors, when allowed to use mastery learning, also achieve a 2-sigma learning gain. Moreover, Bloom's tutors used a larger threshold for mastery than his comparison treatments, which could account for some of their benefits. Nonetheless, the assumption of the human tutor's omnipotence is so widely believed that there is likely to be at least some truth in it.

==Variations (descendants)==
==Generalizations (ascendants)==
==References==
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. ''Educational Researcher'', 13, 4-16.

Corbett, A. (2001). Cognitive computer tutors: Solving the two-sigma problem. In ''User Modeling: Proceedings of the Eighth International Conference'' (pp. 137-147).

Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (in press). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. ''Cognitive Science''.

Cohen, P. A., Kulik, J. A., & Kulik, C.-L. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. ''American Educational Research Journal'', 19(2), 237-248.

Evens, M., & Michael, J. (2006). ''One-on-one Tutoring By Humans and Machines''. Mahwah, NJ: Erlbaum.

Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. ''American Journal of Physics'', 67(9), 819-831.

VanLehn, K. (1998). Analogy events: How examples are used during problem solving. ''Cognitive Science'', 22(3), 347-388.

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? ''Cognitive Science'', 31(1), 3-62.

VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. ''The Journal of the Learning Sciences'', 2(1), 1-59.

VanLehn, K., Lynch, C., Schultz, K., Shapiro, J. A., Shelby, R. H., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. ''International Journal of Artificial Intelligence and Education'', 15(3), 147-204.

[[Category:Glossary]]
[[Category:Instructional Principle]]
[[Category:PSLC General]]

Interaction plateau

2007-12-11T20:51:25Z

Vanlehn: /* Description of principle */