Theory Wiki - User contributions [en]

Ringenberg Ill-Defined Physics

2009-05-15T19:16:52Z

Michael-Ringenberg: /* References */

== Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? ==
''Michael Ringenberg and Kurt VanLehn''

=== Summary Table ===
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Michael Ringenberg (Pitt) & Kurt VanLehn (Pitt)
|-
| '''LearnLab Course''' || Physics
|-
| '''Number of Students''' || ''N'' = 40
|}

=== Abstract ===
Students who complete an introductory physics course often do not
have a good conceptual understanding of the principles taught. There
have been various attempts at increasing conceptual learning, often
with only modest improvements. One promising avenue is the use of
ill-defined problems. However, it can be very difficult for
students to solve these problems without proper support. If
ill-defined problem solving can be supported using intelligent
tutoring systems, then it will be possible to investigate the
potential of ill-defined problems and their influence on conceptual
learning.

=== Background and Significance ===
The goal of a physics course is not to teach how to solve highly constrained \physics problems" but to increase a student's understanding of the world around them and how it can be modeled using equations and formulas. Although students are often good at solving the highly constrained physics problems that are part of homework and exams, they often lack an understanding of the concepts and principles exemplified by those problems [Halloun and Hestenes, 1985].

One possible reason for this is that students develop adequate strategies for solving physics problems, but these strategies are highly procedural and do not rely on a conceptual understanding of the material. There is evidence that students employ shallow problem solving methods. For example, when asked to sort problems, novices will rely on surface features of the problem like what objects (inclines, boxes, pucks, ice, etc.) are used and what quantities are specified (mass, velocity, coefficient of friction, etc.), whereas experts will match problems based on what principles are used to solve them [Chi et al., 1981].

More evidence for shallow problem solving methods includes observed use of examples. When referring back to examples or previously solved problems, novices will focus on matching surface features and try to mimic all the steps presented in the example [VanLehn and Jones, 1993]. However, when supplied with a relevant example that matches the current problem based on the principles used and may or may not match surface features, students tend to rapidly develop better principle-based problem analysis strategies [Ringenberg and VanLehn, 2006].

On the other hand, physics experts tend to solve these (and real-world) problems by first conceptualizing the problem, then forming a qualitative solution, and finally using relevant given quantities to derive a numeric solution [Larkin and Reif, 1979]. When students are required to perform more expert-like problem solving strategies, they can develop better conceptual knowledge. For example, when students are given the task of specifying which physics principles are needed to solve well-defined physics problems as part of their homework and exams (i.e. conceptualizing the problem), their understanding of these concepts improves more than just solving the problems [Leonard et al., 1996].

Unfortunately, simply requiring students to perform more expert-like problem solving strategies may notbe enough. The Andes tutoring system requires students to draw diagrams and specify necessary variables which are steps that experts perform, and students do better on these skills but do not perform any better on "conceptual" skills like principle identification [VanLehn et al., 2005]. A shortcoming of just prescribing and enforcing a problem solving strategy is that students may not see the value of using it and just adapt their shallow methods to include these extra steps.

Having physics students solve more ill-defined problems that are difficult to solve with shallow strategies seems like a viable method for encouraging more conceptual methods. According to [Simon, 1973], "ill-defined" problems covers a broad category of problem types, including problems that are missing necessary information, have multiple equally valid though not equivalent solutions, or have no definitive solution that experts can agree upon. Whereas experts often deal with these sorts of problems, students often have great difficulty solving these problems.

Programs like Activity-Based Physics and Project SCALE-UP break students up into peer groups to solve ill-defined problems. It is hard, however, to determine the effectiveness of the task in such situations as factors such as group size, variations in member's abilities, member's personalities, and gender composition all influence performance measures [Heller and Hollabaugh, 1992]. Maintaining an effective and productive group is very difficult, particularly when a single "bad apple" can negatively affect the entire group's performance [Felps et al., 2006].

Upon analysis of groups that successfully solve ill-defined problems, it was found that these groups tended to be more consistent at following a prescribed problem-solving strategy and are more likely to state the physics concepts and principles being used [Heller et al., 1992]. It should be possible to develop a system that can support solving ill-defined problems by enforcing the use of some better problem-solving strategies.

In the proposed study, participants will be presented with problems that lack some of the contextual clues of standard physics textbook problems. They will not reference idealized objects like "''box A'' slides down an ''incline''." Problems will cover concepts from Newton's Laws, Momentum, or Energy, which should prevent simple guessing on the part of the participants based on what they have studied or used most recently. In addition, participants in the "ill-defined"(experimental) condition will initially have all of the given quantities (mass, velocities, etc.) removed. This causes the problems to become ill-defined in that information necessary for producing a solution is missing. Participants in this condition will be asked to specify what information they would need to solve the problem. After they have done this, they will be provided with the necessary quantities and asked to solve the problem normally. This task is designed to get them to think about what principles they would used to solve the problem and how they are going to apply them. The hints and feedback they receive will facilitate this by asking what principles they plan to use and what would be necessary to use a given principle.

It is expected that participants in the ill-defined condition will develop more expert-like conceptual analysis as their shallow methods are not applicable for this task. This more expert-like conceptual analysis should be easily discernible using a [Dufresne et al., 1992] like problem matching task. In this task, participants are presented with a model problem statement and then asked which of two target problem statements would be solved most similarly to the model. Only one of the targets will use the same principles to solve it, but each of them may share surface features with the model. If participants are doing a "deeper" analysis, then they will select the targets that match based on the principles used to solve it, regardless of the amount of surface similarity. It is expected that engaging the principles in a more conceptual way will lead to greater conceptual understanding, as opposed to just thinking of them as labels for equations. To see if this task encourages a greater conceptual understanding, a multiple-choice conceptual inventory [Singh and Rosengrant, 2003] will be used to assess any gains made over the intervention.

=== Glossary ===

=== Research question ===
Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving?

=== Independent variables ===
* Type of problems solved: [[ill-defined problem]]s vs. [[well-defined problem]]s

For this study, the [[ill-defined problem]]s used lacked key information needed to solve the problem. The problem statements did not include the quantities needed to derive a numeric solution to the problem. Part of the task of solving these problems was to have the participants request the necessary quantities from the system. The system will provide hints and corrective feedback for this task. Once all of the necessary values are elicited by the participant, then the problem becomes a [[well-defined problem]]. The [[well-defined problem]]s used were identical to the [[ill-defined problem]]s except that all of the necessary information was given as part of the problem statement.

Figure 1. Example of one of the ill-defined problems. It is missing key information which would be required to produce a quantitative solution.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress?
|}

Figure 2. Example of the corresponding well-defined version of the problem in Figure 1. It includes all of the necessary and sufficient information needed to produce a quantitative solution to the problem.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress? 
Height of rail: 0.5 m 
Regina's velocity when she leaves the rail: 0.45 m/s
|}

=== Hypothesis ===
If students are
required to figure out what information is needed to solve ill-defined
physics problems before solving them, then they will develop better
conceptual understanding than if they had been presented with the same
problems with all the necessary information provided.

=== Dependent variables ===
* [[Normal post-test]]
** Multiple-choice conceptual questions
* [[Transfer]]
** Judgement task
*** Problem matching task: participants are given a target problem statement and are asked which of two additional problem statements are solved most similarly to the target problem without solving any of the problems (Dufresne, Gerace, Hardiamnn, & Mestre, 1992).
* Performance on [[Andes]] problems
** Solution times
** Error rates
** Help requests

=== Expected Findings ===
Participants who solve the ill-defined versions of the problems will:
* Perform better on conceptual questions.
* Perform better on the problem matching task.
* Have faster solution times.
* Have lower error rates.
* Have fewer help requests.

=== Explanation ===
Because the experimental participants will be required to engage in more conceptual analysis of the problems, they will more deeply analyze and encode the [[knowledge component]]s used in the problems. This will lead to better performance on tasks that use this better encoding. It will also have effects on problem solving because with the conceptual analysis done before problem solving, there will be less floundering and help abuse during problem solving.

=== Further Information ===
==== Annotated bibliography ====

* Paper and poster presented at ITS 2008 Conference (young researcher's track).

==== References ====
*[Chi et al., 1981] Chi, M. T. H., Feltovich, P., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5:121-152.

*[Dufresne et al., 1992] Dufresne, R. J., Gerace, W. J., Hardiman, P. T., and Mestre, J. P. (1992). Con- straining novices to perform expertlike problem analyses: Effects on schema acquisition. Journal of the Learning Sciences, 2(3):307-331.

*[Felps et al., 2006] Felps, W., Mitchell, T. R., and Byington, E. (2006). How, when, and why bad apples spoil the barrel: Negative group members and dysfunctional groups. Research in Organizational Behavior, 27:175-222.

*[Halloun and Hestenes, 1985] Halloun, I. A. and Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11):1043-1055.

*[Heller and Hollabaugh, 1992] Heller, P. and Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. part 2: Designing problems and structuring groups. American Journal of Physics, 60(7):637-644.

*[Heller et al., 1992] Heller, P., Keith, R., and Anderson, S. (1992). Teaching problem solving through cooperative grouping. part 1: Group versus individual problem solving. American Journal of Physics, 60(7):627-636.

*[Larkin and Reif, 1979] Larkin, J. H. and Reif, F. (1979). Understanding and teaching problem-solving in physics. International Journal of Science Education, 1(2):191-203.

*[Leonard et al., 1996] Leonard, W. J., Dufresne, R. J., and Mestre, J. P. (1996). Using qualitative problem- solving strategies to highlight the role of conceptual knowledge in solving problems. American Journal of Physics, 64(12):1495-1503.

*[Ringenberg and VanLehn, 2006] Ringenberg, M. A. and VanLehn, K. (2006). Scaffolding problem solving with annotated, worked-out examples to promote deep learning. In Ikeda, M., Ashley, K., and Chan, T.- W., editors, ITS 2006, LNCS 4053, pages 625-634, Taiwan. Springer-Verlag Berlin Heidelberg. Winner of Best Paper First Authored by a Student Award.

*[Simon, 1973] Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4(3):181- 201.

*[Singh and Rosengrant, 2003] Singh, C. and Rosengrant, D. (2003). Multiple-choice test of energy and momentum concepts. American Journal of Physics, 71(6):607-617.

*[VanLehn and Jones, 1993] VanLehn, K. and Jones, R. M. (1993). Better learners use analogical problem solving sparingly. In Utgoff, P. E., editor, Proceedings of the Tenth International Conference on Machine Learning, pages 338-345, San Mateo, CA. Morgan Kaufmann.

*[VanLehn et al., 2005] VanLehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., and Wintersgill, M. (2005). Andes physics tutoring system: Five years of evaluations. In McCalla, G., Looi, C. K., Bredeweg, B., and Breuker, J., editors, Artificial Intelligence in Education, pages 678-685, Amsterdam, Netherlands. IOS Pres.

==== Connections ====

==== Future plans ====

[[Category:Study]]

Ringenberg Ill-Defined Physics

2009-05-15T19:13:36Z

Michael-Ringenberg: /* Background and Significance */

== Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? ==
''Michael Ringenberg and Kurt VanLehn''

=== Summary Table ===
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Michael Ringenberg (Pitt) & Kurt VanLehn (Pitt)
|-
| '''LearnLab Course''' || Physics
|-
| '''Number of Students''' || ''N'' = 40
|}

=== Abstract ===
Students who complete an introductory physics course often do not
have a good conceptual understanding of the principles taught. There
have been various attempts at increasing conceptual learning, often
with only modest improvements. One promising avenue is the use of
ill-defined problems. However, it can be very difficult for
students to solve these problems without proper support. If
ill-defined problem solving can be supported using intelligent
tutoring systems, then it will be possible to investigate the
potential of ill-defined problems and their influence on conceptual
learning.

=== Background and Significance ===
The goal of a physics course is not to teach how to solve highly constrained \physics problems" but to increase a student's understanding of the world around them and how it can be modeled using equations and formulas. Although students are often good at solving the highly constrained physics problems that are part of homework and exams, they often lack an understanding of the concepts and principles exemplified by those problems [Halloun and Hestenes, 1985].

One possible reason for this is that students develop adequate strategies for solving physics problems, but these strategies are highly procedural and do not rely on a conceptual understanding of the material. There is evidence that students employ shallow problem solving methods. For example, when asked to sort problems, novices will rely on surface features of the problem like what objects (inclines, boxes, pucks, ice, etc.) are used and what quantities are specified (mass, velocity, coefficient of friction, etc.), whereas experts will match problems based on what principles are used to solve them [Chi et al., 1981].

More evidence for shallow problem solving methods includes observed use of examples. When referring back to examples or previously solved problems, novices will focus on matching surface features and try to mimic all the steps presented in the example [VanLehn and Jones, 1993]. However, when supplied with a relevant example that matches the current problem based on the principles used and may or may not match surface features, students tend to rapidly develop better principle-based problem analysis strategies [Ringenberg and VanLehn, 2006].

On the other hand, physics experts tend to solve these (and real-world) problems by first conceptualizing the problem, then forming a qualitative solution, and finally using relevant given quantities to derive a numeric solution [Larkin and Reif, 1979]. When students are required to perform more expert-like problem solving strategies, they can develop better conceptual knowledge. For example, when students are given the task of specifying which physics principles are needed to solve well-defined physics problems as part of their homework and exams (i.e. conceptualizing the problem), their understanding of these concepts improves more than just solving the problems [Leonard et al., 1996].

Unfortunately, simply requiring students to perform more expert-like problem solving strategies may notbe enough. The Andes tutoring system requires students to draw diagrams and specify necessary variables which are steps that experts perform, and students do better on these skills but do not perform any better on "conceptual" skills like principle identification [VanLehn et al., 2005]. A shortcoming of just prescribing and enforcing a problem solving strategy is that students may not see the value of using it and just adapt their shallow methods to include these extra steps.

Having physics students solve more ill-defined problems that are difficult to solve with shallow strategies seems like a viable method for encouraging more conceptual methods. According to [Simon, 1973], "ill-defined" problems covers a broad category of problem types, including problems that are missing necessary information, have multiple equally valid though not equivalent solutions, or have no definitive solution that experts can agree upon. Whereas experts often deal with these sorts of problems, students often have great difficulty solving these problems.

Programs like Activity-Based Physics and Project SCALE-UP break students up into peer groups to solve ill-defined problems. It is hard, however, to determine the effectiveness of the task in such situations as factors such as group size, variations in member's abilities, member's personalities, and gender composition all influence performance measures [Heller and Hollabaugh, 1992]. Maintaining an effective and productive group is very difficult, particularly when a single "bad apple" can negatively affect the entire group's performance [Felps et al., 2006].

Upon analysis of groups that successfully solve ill-defined problems, it was found that these groups tended to be more consistent at following a prescribed problem-solving strategy and are more likely to state the physics concepts and principles being used [Heller et al., 1992]. It should be possible to develop a system that can support solving ill-defined problems by enforcing the use of some better problem-solving strategies.

In the proposed study, participants will be presented with problems that lack some of the contextual clues of standard physics textbook problems. They will not reference idealized objects like "''box A'' slides down an ''incline''." Problems will cover concepts from Newton's Laws, Momentum, or Energy, which should prevent simple guessing on the part of the participants based on what they have studied or used most recently. In addition, participants in the "ill-defined"(experimental) condition will initially have all of the given quantities (mass, velocities, etc.) removed. This causes the problems to become ill-defined in that information necessary for producing a solution is missing. Participants in this condition will be asked to specify what information they would need to solve the problem. After they have done this, they will be provided with the necessary quantities and asked to solve the problem normally. This task is designed to get them to think about what principles they would used to solve the problem and how they are going to apply them. The hints and feedback they receive will facilitate this by asking what principles they plan to use and what would be necessary to use a given principle.

It is expected that participants in the ill-defined condition will develop more expert-like conceptual analysis as their shallow methods are not applicable for this task. This more expert-like conceptual analysis should be easily discernible using a [Dufresne et al., 1992] like problem matching task. In this task, participants are presented with a model problem statement and then asked which of two target problem statements would be solved most similarly to the model. Only one of the targets will use the same principles to solve it, but each of them may share surface features with the model. If participants are doing a "deeper" analysis, then they will select the targets that match based on the principles used to solve it, regardless of the amount of surface similarity. It is expected that engaging the principles in a more conceptual way will lead to greater conceptual understanding, as opposed to just thinking of them as labels for equations. To see if this task encourages a greater conceptual understanding, a multiple-choice conceptual inventory [Singh and Rosengrant, 2003] will be used to assess any gains made over the intervention.

=== Glossary ===

=== Research question ===
Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving?

=== Independent variables ===
* Type of problems solved: [[ill-defined problem]]s vs. [[well-defined problem]]s

For this study, the [[ill-defined problem]]s used lacked key information needed to solve the problem. The problem statements did not include the quantities needed to derive a numeric solution to the problem. Part of the task of solving these problems was to have the participants request the necessary quantities from the system. The system will provide hints and corrective feedback for this task. Once all of the necessary values are elicited by the participant, then the problem becomes a [[well-defined problem]]. The [[well-defined problem]]s used were identical to the [[ill-defined problem]]s except that all of the necessary information was given as part of the problem statement.

Figure 1. Example of one of the ill-defined problems. It is missing key information which would be required to produce a quantitative solution.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress?
|}

Figure 2. Example of the corresponding well-defined version of the problem in Figure 1. It includes all of the necessary and sufficient information needed to produce a quantitative solution to the problem.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|Regina is practising skateboard tricks. She grinds her board along a horizontal rail and falls of the end onto a mattress she placed there. How fast is she travelling just before hitting the mattress? 
Height of rail: 0.5 m 
Regina's velocity when she leaves the rail: 0.45 m/s
|}

=== Hypothesis ===
If students are
required to figure out what information is needed to solve ill-defined
physics problems before solving them, then they will develop better
conceptual understanding than if they had been presented with the same
problems with all the necessary information provided.

=== Dependent variables ===
* [[Normal post-test]]
** Multiple-choice conceptual questions
* [[Transfer]]
** Judgement task
*** Problem matching task: participants are given a target problem statement and are asked which of two additional problem statements are solved most similarly to the target problem without solving any of the problems (Dufresne, Gerace, Hardiamnn, & Mestre, 1992).
* Performance on [[Andes]] problems
** Solution times
** Error rates
** Help requests

=== Expected Findings ===
Participants who solve the ill-defined versions of the problems will:
* Perform better on conceptual questions.
* Perform better on the problem matching task.
* Have faster solution times.
* Have lower error rates.
* Have fewer help requests.

=== Explanation ===
Because the experimental participants will be required to engage in more conceptual analysis of the problems, they will more deeply analyze and encode the [[knowledge component]]s used in the problems. This will lead to better performance on tasks that use this better encoding. It will also have effects on problem solving because with the conceptual analysis done before problem solving, there will be less floundering and help abuse during problem solving.

=== Further Information ===
==== Annotated bibliography ====

* Paper and poster presented at ITS 2008 Conference (young researcher's track).

==== References ====
*Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
*Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). Constraining Novices to Perform Expertlike Problem Analyses: Effects on Schema Acquisition. Journal of the Learning Sciences, 2(3), 307-331.
*Ge, X., & Land, S. M. (2003). Scaffolding students' problem-solving processes in an ill-structured task using question prompts and peer interactions. [Article]. Etr\&D-Educational Technology Research and Development, 51(1), 21-38.
*Halloun, I. A., & Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043-1055.
*Heller, P., & Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. Part 2: Designing problems and structuring groups. American Journal of Physics, 60(7), 637-644.
*Larkin, J. H., & Reif, F. (1979). Understanding and Teaching Problem-Solving in Physics. International Journal of Science Education, 1(2), 191-203.
*Leonard, W. J., Dufresne, R. J., & Mestre, J. P. (1996). Using qualitative problem-solving strategies to highlight the role of conceptual knowledge in solving problems. [Article]. American Journal of Physics, 64(12), 1495-1503.
*VanLehn, K., & Jones, R. M. (1993). Better learners use analogical problem solving sparingly. Paper presented at the Proceedings of the Tenth International Conference on Machine Learning, San Mateo, CA.

==== Connections ====

==== Future plans ====

[[Category:Study]]

Ringenberg Ill-Defined Physics

2009-05-15T18:44:35Z

Michael-Ringenberg: /* Annotated bibliography */

Ringenberg Examples-as-Help

2008-10-08T15:40:19Z

Michael-Ringenberg: /* Independent variables */

==Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning==
''Michael Ringenberg and Kurt VanLehn''

===Summary Table===
{| border="1" cellpsacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Kurt VanLehn, Donald Treacy, Michael Ringenberg
|-
| '''Study Start Date''' || 18 February 2005
|-
| '''Study End Date''' || 04 April 2005
|-
| '''LearnLab Site''' || USNA
|-
| '''LearnLab Course''' || General Physics II
|-
| '''Number of Students''' || ''N'' = 46
|-
| '''Total Participant Hours''' || 20 minutes over required coursework
|-
| '''DataShop''' || No; Andes data still incompatible
|}

===Abstract===
This ''in vivo'' experiment which occurred in the Physics LearnLab compared the relative utility of an intelligent tutoring system that used [[hint sequence]]s to a version that used [[completely justified example]]s for learning college level physics. In order to test which strategy produced better gains in competence, two version of [[Andes]] were used: one offered participants hint sequences and the other completely justified examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.

===Background and Significance===
When students use a tutoring system with hint sequences, they sometimes engage in [[help abuse]] on virtually every [[step]] (citation needed). This means that the tutoring system is telling them each step, so essentially, they are generating a worked-out example. There may be nothing wrong with this for some students, as examples can be effective instructional material (citation needed).

===Glossary===
See [[:Category:Ringenberg Examples-as-Help|Ringenberg Examples-as-Help Glossary]]

===Research question===
Will robust learning ensue if students are presented with relevant, [[completely justified example]]s instead of [[hint sequence]]s whenever they ask for a help?

===Independent variables===
Particpants worked on assigned homework problems covering Inductors by using Andes at home. When they requested help on a step, they got either:

* a relevant, completely justified example (the ''Examples'' condition), or

* the normal Andes hint sequence (the ''Hints'' condition).

When they clicked on the "Done" button the example or the hint would disappear, then they would be back in problem solving mode. Thus, Examples students could not easily copy steps from the example to the problem they were solving.

Figure 1: A screenshot of Andes Physics Workbench
[[Image:AndesScreenLR1b.jpg]]

Figure 2: A worked-out example. A window would pop-up containing a relevant example if a participant in the experimental condition asked for help while solving problems in Andes. This is the example that was paired with the problem in Figure 1. Each '''Source''' field in the equation table was either a list of the indexes to the equations combined or simplified to produce the given equation or the name of the principle used. The principle name were linked to a textbook page covering the topic and the pages were available to all participants. The ''italic'' text in the '''Source''' field was a tooltip that would appear if the participant moused over the source. Bold equations indicate sought quantities.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|
; Problem Statement : In the circuit below, the current through the resistor rises from zero at 0.0 s to 40% of its maximum value at 4.0 s. The inductor has a self-inductance of 10H and the battery has a Vb of 12 V. What is the resistance of R1? What is the current through R1 at 4.0 s?
[[Image:LR1.jpg]]
; Solution
: '''Variables'''
:* T0 = switch is closed
:* T1 = 4.0 s later
:* T2 = "infinity" (a long time later)
:* L1 = inductance of L1
:* R1 = Resistance of R1
:* Vb = Voltage across BaE1 at time T0 to T2
:* τ = time constant of circuit containing L1
:* t = duration of time from T0 to T1
:* I1 = Current through R1 at time T1
:* I2 = Current through R1 at time T2
: '''Equations'''
{| border="1" cellspacing="0" cellpadding="5" align="center"
!
! Equation
! Source
|-
| 1.
| L1 = 10 H
| Given ''(This information is from the problem statement)''
|-
| 2.
| Vb = 12 V
| Given ''(This information is from the problem statement.)''
|-
| 3.
| t = 4 s
| Given ''(This information is from the problem statement.)''
|-
| 4.
| L1 = 10 H
| Given ''(This information is from the problem statement.)''
|-
| 5.
| I1 = 0.4 * I2
| Given ''(This information is from the problem statement.)''
|-
| 6.
| I1 = I2 * (1-exp(-t/τ))
| LR current growth ''(I=Ifull * (1-e(-t/τ)))''
|-
| 7.
| 0.4 * I2 = I2 * (1-exp(-t/τ))
| 6,5
|-
| 8.
| 0.4 = (1-exp(-4 s/τ))
| 7,3
|-
| 9.
| τ = 0.128 s
| 8
|-
| 10.
| τ = L1/R1
| LR Time constant ''(τ = L / R)''
|-
| 11.
| 0.128 s = 10 H/R1
| 10,9,1
|-
| 12.
| '''R1 = 78.3 ohm'''
| 11
|-
| 13.
| I2 = Vb/R1
| LR current fully charged ''(Ohm's Law: I = Vb / R)''
|-
| 14.
| I2 = 12 V/78.3 ohm
| 13,2,11
|-
| 15.
| I2 = 0.153 A
| 14
|-
| 16.
| I1 = 0.4 * 0.153 A
| 5,14
|-
| 17.
| '''I1 = 0.0612 A'''
| 16
|}
|}

Figure 3: A Hint sequence from Andes. This is a possible hint sequence which is displayed in the lower left hand window of the Andes Physics Workbench if a participant asked for help in the control condition. "T:" indicates messages from the tutoring system. "S:" indicates input from the student/participant via menus.
<pre>
T: You should finish entering all of the useful given quantities in
the problem. Why don't you work on entering the given value of the
inductance of L1.
Explain further OK

T: You can find the value of the inductance of L1 in the problem
statement.

T: The value of the inductance of L1 is given as 3.2 H.

T: Enter the equation L1 = 3.2 H.
OK
</pre>

Figure 4: Another Andes hint sequence. Again, this is a possible hint sequence available to participants in the control condition if they asked for help. "T:" indicates messages from the tutoring system. "S:" indicates input from the student/participant via menus.
<pre>
T: Now that you have stated all of the given information, you should
start on the major principles. What quantity is the problem seeking?
S: The Voltage across L1 at time T0 to T1.

T: That's right. What is the first principle application that you
would like to work on? Hint: this principle application will
usually be one that mentions the sought quantity
explicity. Therefore it's equation may contain the sought quantity
that the problem seeks.
S: V= -L*dI/dt Inductor EMF
</pre>

===Hypothesis===
Providing completely justified examples instead of hint sequences when students ask for help on steps will promote the learning of knowledge components and help appropriately generalize the knowledge components.

===Dependent variables & Results===
;Near Transfer, retention:
:Performance on problems involving inductors on the normal mid-term exam that were similar to the training problems. There was not significant difference in performance between the two conditions. Both conditions did better than a baseline of participants who solved no homework problems.
;Transfer task, deep structure assessment
:Problem matching task: No significant difference in performance between the two conditions; however, participants in the ''examples'' condition solved fewer training problems. Both conditions did better than a baseline of participants who solved no homework problems.
;Homework:
:Number of problems completed: Participants in the ''examples'' condition solved significantly fewer problems than participants in the ''hints'' condition.
:Time on task: Participants in the ''examples'' condition spent less time solving problems than those in the ''hints'' condition. Participants in both conditions spent about the same amount of time per problem.

===Explanation===

===Annotated bibliography===
* Ringenberg, Michael A. & VanLehn, Kurt (2006). ''Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning.'' Paper presented at the ITS 2006, Taiwan. Winner of Best Paper First Authored by a Student Award. [http://www.pitt.edu/~vanlehn/Stringent/PDF/06ITS_MR_KVL.pdf 231Kb PDF]

===References===

[[Category:Study]]

File:LR1.jpg

2008-10-08T15:39:49Z

Michael-Ringenberg: A circuit diagram containing an inductor and a resistor.

A circuit diagram containing an inductor and a resistor.

File:AndesScreenLR1b.jpg

2008-10-08T15:37:18Z

Michael-Ringenberg: Screenshot from Andes featuring problem LR1b from the circuits curriculum.

Screenshot from Andes featuring problem LR1b from the circuits curriculum.

Ringenberg Examples-as-Help

2008-10-08T15:17:21Z

Michael-Ringenberg: /* Independent variables */

==Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning==
''Michael Ringenberg and Kurt VanLehn''

===Summary Table===
{| border="1" cellpsacing="0" cellpadding="5" style="text-align: left;"
| '''PIs''' || Kurt VanLehn, Donald Treacy, Michael Ringenberg
|-
| '''Study Start Date''' || 18 February 2005
|-
| '''Study End Date''' || 04 April 2005
|-
| '''LearnLab Site''' || USNA
|-
| '''LearnLab Course''' || General Physics II
|-
| '''Number of Students''' || ''N'' = 46
|-
| '''Total Participant Hours''' || 20 minutes over required coursework
|-
| '''DataShop''' || No; Andes data still incompatible
|}

===Abstract===
This ''in vivo'' experiment which occurred in the Physics LearnLab compared the relative utility of an intelligent tutoring system that used [[hint sequence]]s to a version that used [[completely justified example]]s for learning college level physics. In order to test which strategy produced better gains in competence, two version of [[Andes]] were used: one offered participants hint sequences and the other completely justified examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.

===Background and Significance===
When students use a tutoring system with hint sequences, they sometimes engage in [[help abuse]] on virtually every [[step]] (citation needed). This means that the tutoring system is telling them each step, so essentially, they are generating a worked-out example. There may be nothing wrong with this for some students, as examples can be effective instructional material (citation needed).

===Glossary===
See [[:Category:Ringenberg Examples-as-Help|Ringenberg Examples-as-Help Glossary]]

===Research question===
Will robust learning ensue if students are presented with relevant, [[completely justified example]]s instead of [[hint sequence]]s whenever they ask for a help?

===Independent variables===
Particpants worked on assigned homework problems covering Inductors by using Andes at home. When they requested help on a step, they got either:

* a relevant, completely justified example (the ''Examples'' condition), or

* the normal Andes hint sequence (the ''Hints'' condition).

When they clicked on the "Done" button the example or the hint would disappear, then they would be back in problem solving mode. Thus, Examples students could not easily copy steps from the example to the problem they were solving.

Figure 1: A screenshot of Andes Physics Workbench
[[Image:AndesScreenLR1b.jpg]]

Figure 2: A worked-out example. A window would pop-up containing a relevant example if a participant in the experimental condition asked for help while solving problems in Andes. This is the example that was paired with the problem in Figure 1. Each '''Source''' field in the equation table was either a list of the indexes to the equations combined or simplified to produce the given equation or the name of the principle used. The principle name were linked to a textbook page covering the topic and the pages were available to all participants. The ''italic'' text in the '''Source''' field was a tooltip that would appear if the participant moused over the source. Bold equations indicate sought quantities.
{| border="1" cellspacing="0" cellpadding="5" align="center"
|
; Problem Statement : In the circuit below, the current through the resistor rises from zero at 0.0 s to 40% of its maximum value at 4.0 s. The inductor has a self-inductance of 10H and the battery has a Vb of 12 V. What is the resistance of R1? What is the current through R1 at 4.0 s?
[[Image:LR1.png]]
; Solution
: '''Variables'''
:* T0 = switch is closed
:* T1 = 4.0 s later
:* T2 = "infinity" (a long time later)
:* L1 = inductance of L1
:* R1 = Resistance of R1
:* Vb = Voltage across BaE1 at time T0 to T2
:* τ = time constant of circuit containing L1
:* t = duration of time from T0 to T1
:* I1 = Current through R1 at time T1
:* I2 = Current through R1 at time T2
: '''Equations'''
{| border="1" cellspacing="0" cellpadding="5" align="center"
!
! Equation
! Source
|-
| 1.
| L1 = 10 H
| Given ''(This information is from the problem statement)''
|-
| 2.
| Vb = 12 V
| Given ''(This information is from the problem statement.)''
|-
| 3.
| t = 4 s
| Given ''(This information is from the problem statement.)''
|-
| 4.
| L1 = 10 H
| Given ''(This information is from the problem statement.)''
|-
| 5.
| I1 = 0.4 * I2
| Given ''(This information is from the problem statement.)''
|-
| 6.
| I1 = I2 * (1-exp(-t/τ))
| LR current growth ''(I=Ifull * (1-e(-t/τ)))''
|-
| 7.
| 0.4 * I2 = I2 * (1-exp(-t/τ))
| 6,5
|-
| 8.
| 0.4 = (1-exp(-4 s/τ))
| 7,3
|-
| 9.
| τ = 0.128 s
| 8
|-
| 10.
| τ = L1/R1
| LR Time constant ''(τ = L / R)''
|-
| 11.
| 0.128 s = 10 H/R1
| 10,9,1
|-
| 12.
| '''R1 = 78.3 ohm'''
| 11
|-
| 13.
| I2 = Vb/R1
| LR current fully charged ''(Ohm's Law: I = Vb / R)''
|-
| 14.
| I2 = 12 V/78.3 ohm
| 13,2,11
|-
| 15.
| I2 = 0.153 A
| 14
|-
| 16.
| I1 = 0.4 * 0.153 A
| 5,14
|-
| 17.
| '''I1 = 0.0612 A'''
| 16
|}
|}

Figure 3: A Hint sequence from Andes. This is a possible hint sequence which is displayed in the lower left hand window of the Andes Physics Workbench if a participant asked for help in the control condition. "T:" indicates messages from the tutoring system. "S:" indicates input from the student/participant via menus.
<pre>
T: You should finish entering all of the useful given quantities in
the problem. Why don't you work on entering the given value of the
inductance of L1.
Explain further OK

T: You can find the value of the inductance of L1 in the problem
statement.

T: The value of the inductance of L1 is given as 3.2 H.

T: Enter the equation L1 = 3.2 H.
OK
</pre>

Figure 4: Another Andes hint sequence. Again, this is a possible hint sequence available to participants in the control condition if they asked for help. "T:" indicates messages from the tutoring system. "S:" indicates input from the student/participant via menus.
<pre>
T: Now that you have stated all of the given information, you should
start on the major principles. What quantity is the problem seeking?
S: The Voltage across L1 at time T0 to T1.

T: That's right. What is the first principle application that you
would like to work on? Hint: this principle application will
usually be one that mentions the sought quantity
explicity. Therefore it's equation may contain the sought quantity
that the problem seeks.
S: V= -L*dI/dt Inductor EMF
</pre>

===Hypothesis===
Providing completely justified examples instead of hint sequences when students ask for help on steps will promote the learning of knowledge components and help appropriately generalize the knowledge components.

===Dependent variables & Results===
;Near Transfer, retention:
:Performance on problems involving inductors on the normal mid-term exam that were similar to the training problems. There was not significant difference in performance between the two conditions. Both conditions did better than a baseline of participants who solved no homework problems.
;Transfer task, deep structure assessment
:Problem matching task: No significant difference in performance between the two conditions; however, participants in the ''examples'' condition solved fewer training problems. Both conditions did better than a baseline of participants who solved no homework problems.
;Homework:
:Number of problems completed: Participants in the ''examples'' condition solved significantly fewer problems than participants in the ''hints'' condition.
:Time on task: Participants in the ''examples'' condition spent less time solving problems than those in the ''hints'' condition. Participants in both conditions spent about the same amount of time per problem.

===Explanation===

===Annotated bibliography===
* Ringenberg, Michael A. & VanLehn, Kurt (2006). ''Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning.'' Paper presented at the ITS 2006, Taiwan. Winner of Best Paper First Authored by a Student Award. [http://www.pitt.edu/~vanlehn/Stringent/PDF/06ITS_MR_KVL.pdf 231Kb PDF]

===References===

[[Category:Study]]

Ringenberg Ill-Defined Physics

2008-10-08T12:53:20Z

Michael-Ringenberg: /* Independent variables */

Interactive Communication

2008-04-16T15:03:01Z

Michael-Ringenberg: /* 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, 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]]

Ringenberg Ill-Defined Physics

2008-04-16T14:58:57Z

Michael-Ringenberg:

Ringenberg Ill-Defined Physics

2008-04-16T14:57:40Z

Michael-Ringenberg: New page: == Does Solving Ill-Defined Physics Problems Elicit More Learning than Conventional Problem Solving? == ''Michael Ringenberg and Kurt VanLehn'' === Summary Table === {| border="1" cellsp...

Ringenberg Examples-as-Help

2007-04-17T20:14:59Z

Michael-Ringenberg: /* Summary Table */

Ringenberg Examples-as-Help

2007-04-17T20:11:29Z

Michael-Ringenberg:

Talk:Does learning from worked-out examples improve tutored problem solving?

2006-11-20T22:02:48Z

Michael-Ringenberg:

In the Explanation section, I think that you could be a little clearer about what you expect the participants to learn. Are they increasing their deep feature perception? Are they refining their knowledge components?

I think it would be a very good idea to fill in the glossary items, particularly "Self-explanation." I am not sure that your definition would completely match Chi's as she has defined them in the context of example study to be either derivational or procedural (where does this step come from or why was this step done). It seems that the self-explanations in this are concerned more with the derivational type, which leads me to believe that this study is concerned more with "deep feature perception" as the underlying important feature from the PSLC framework.

I would also argue that in the event space tree, if a student enters a correct step but used shallow strategies, like in 1.2 and 4.2, then they learned, it is just that they did not learn or reinforce the correct knowledge component.

I think it would also be interesting to add an analysis of the logfiles to look at the behavior of the students. If I understand your theory correctly, then it should be the case that the problem solving only students would need more assistance from the tutor, particularly at the beginning than the example studiers.

I also think that further specifying the dependent variables and how you expect each group to perform on the measures will help clarify the study. For example, if the conceptual knowledge task is simply solving similar items on a test a day later, then you should say so and that you, for example, expect the performance of both groups to be similar.

Interactive Communication

2006-10-09T18:53:23Z

Michael-Ringenberg: /* Descendents */

== The PSLC Interactive Communication cluster ==

=== Abstract ===
The studies in the Interactive Communication deal primarily with learning environments where there are two agents, one of which is the student. The other agent is typically a second student, a human tutor or a tutoring system. Both agents are capable of doing the instructional activity, albeit with varying degrees of success. They communicate, either in a natural language or a formal language, such as mathematical expression or menus. The main variables are:

*What part of the work is done by which agent? On one extreme, the student does all the work while the other agent watches. On the other extreme, the student watches while the other agent does all the work. In the middle, the two agents collaborate somehow.
*Who makes the choice about which work is done by which agent? The student, the other agent or a fixed policy of some kind?

Our hypothesis is that learning by doing is the best, except that as the student takes on more work or more challenging work, the error frequency or the time to recover from errors may begin to interfere with learning. Communication also can interfere when learning, in that it takes time and cognitive resources, and that it is never perfect. Thus, learning can be optimized by somehow balancing the work done by the student, the work done by the agent and the work done by both in communicating.

=== Background and Significance ===
Educational dialogue has mostly been studied in classrooms (e.g., Lave & Wenger, 1991; Leinhardt, 1990) and workplaces (e.g., Hutchins, 1995; Nunes, Schliemann & Carraher, 1993). In order to investigate more tractable albeit still complex situations, most of our research focuses on dyadic 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.

Some studies of naturally occurring dyadic dialogues (e.g., Fox, 1993; Graesser, Bowers, Hacker, & Person, 1997; MacArthur, Stasz, & Zmuidzinas, 1990) sought their underlying structure. They found that the dialogue structure was strongly determined by the task that the participants were working on. For instance, if the task was solving a problem, then both dyads and students working alone tended to follow paths in the problem space.

Other studies compared the learning gains of dyadic dialogue-based instruction to non-interactive instruction from text, video, etc. (e.g., VanLehn, Graesser et al., in press; Katz, Connelly & Allbritton, 2003; Cohen, Kulik & Kulik, 1982). These studies found surprisingly mixed results. Although most studies showed that interactive communication was more effective than less interactive instruction, it was not always better than non-interactive instruction.

Having preliminary answers to the research questions of what dialogue is and whether it is effective, the next step in this important line of research is to determine when different types of interactive communication are effective and why.
The studies in the Interactive Communication cluster tend

=== Glossary ===
To be developed, but will probably include:

* ''Agent'': Something that can perform the instructional activity. Typically a student, a tutor, a tutoring system or a simulated student. In the extreme case, an agent can be a passive medium, such as text or a video, that presents a performance of the activity. For instance, if the instructional activity is solving physics problems, then a worked example, such as the ones shown in a textbook, is an agent.

* ''Communication''.

* ''Initiative''. This measures the ratio of the work initiated by the two agents. A dialogue with lots of student initiative is one where the student spontaneously initiates work on the activity. A dialogue with lots of tutor initiative is one where the tutor either does the work or requests (in the speech act sense of “request”) the student to do the work. The “initiative” term comes from linguistics, whereas a synonymous distinction, learn control vs. teacher control, comes from education.

* ''Zone of proximal development''. When instruction is laid out on a scale of difficulty from easy to hard, there is a region where the instruction is too hard for the student to learn effectively from it without help, but still just easy enough that the student can learn if given help, typically from a second agent. This region is called the zone of proximal development (ZPD), a term from developmental psychology.

=== Research question ===
How can instructional activities that involve two agents, the student and another agent, increase robust learning?

=== Independent ===
* The type of second agent (peer, tutor, computer program, passive media) and how it communicates with the student,
* the allocation of work between the two agents,
* how that schedule is controlled,
* and the difficulty of the instruction.

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

=== Hypothesis ===
When student engage in collaborative learning with another agent where the collaboration somehow appropriately balances the work done by the agents and their communication, then learning will be more robust than it would if the learning environment had just the student and not the second agent.

=== Explanation ===
Assuming a control condition where the student works alone or with only limited interaction with the second agent, there are 3 cases:

#If the instruction is in the students’ zone of proximal development (ZPD), then a second agent’s help can increase learning compared to a control condition.
#If the instruction above (more difficult than) the ZPD, then the student makes too many errors and/or requires too much communication with the second agent, which thwarts learning. Thus, learning is equally ineffective in the two conditions.
#If the instruction is below (more easy than) the ZPD, then the student can learn just as much working alone as when working with the second agent. That is, learning is equally effective in the two conditions.

This idea can be rephrased in terms of the PSLC’s [[Root_node|general hypothesis]]. Robust learning should occur under two conditions. First, the instruction should be designed to have the right paths, which means that there is a target path that involves the student doing almost all the intellectual work (learning by doing) and many alternative paths where in the second agent does most of the work. Second, the student should choose the paths so that they take the learning-by-doing path by default, and take the other paths when the learning-by-doing path is too difficult for this particular student at this time. Moreover, the choice of taking an alternative to the learning-by-doing path should take into account the overhead and reliability of communication, which is generally higher on the alternative paths.

=== Descendents ===

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

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

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

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

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

*[[Reflective Dialogues (Katz)]]

*[[Post-practice reflection (Katz)]]

*[[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)]]

*[[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Moved to Refinement and Fluency, Was in Coordinative Learning]

*[[FrenchCulture|Understanding culture from film (Ogan, Aleven & Jones)]]

*Does learning from examples improved tutored problem solving? (Renkl, Aleven & Salden) [Was in Coordinative Learning]

*[[Visual-Verbal Learning (Aleven & Butcher Project) | Visual-Verbal Learning (Aleven & Butcher)]] -- ''Elaborated Explanation condition is the relevant manipulation''

*The self-correction of speech errors (McCormick, O’Neill & Siskin) [Was in Fluency and in Coordinative Learning]

*[[Ringenberg_Examples-as-Help | Scaffolding Problem Solving with Embedded Example to Promote Deep Learning (Ringenberg & VanLehn)]]

=== Annotated bibliography ===
Forthcoming

[[Category:Cluster]]

Ringenberg Examples-as-Help

2006-10-09T18:49:14Z

Michael-Ringenberg: Scaffolding problem solving with examples or hint sequences.

==Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning==
''Michael Ringenberg and Kurt VanLehn''

===Abstract===
This ''in vivo'' experiment which occurred in the Physics LearnLab compared the relative utility of an intelligent tutoring system that used procedure-based hints to a version that used worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two version of [http://www.andes.pitt.edu/ Andes] were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in therms of the number of problems it took to obtain the same level of mastery.

===Background and Significance===
In this study, we were investigating different types of content an intelligent tutoring system agent could give a student. The system used for this investigation was the highly successful [http://www.andes.pitt.edu Andes] system that uses targeted hint sequences when students ask it for help. If students find these hints confusing because they lack the proper knowledge components, then they have been observed to engage in help abuse to construct a worked-out example. Noting that examples can be effective aids in instructional material, we wished to see if they could be effectively used during problem solving in order to foster the learning of knowledge components. We suspect that examples would be effective because they are content rich and can be used effectively by varying competence levels and therefor more likely to hit the students Zone of Proximal Development than targeted hints.

===Glossary===
;Annotated, Worked-out Example
:An problem statement or description and the steps necessary to solve it as demonstrated by an expert where each step is annotated with the name of the principle used to generate it.
;Problem matching task
:Participants are presented with a model problem statement and then asked which of two alternate problem statements would be solved most similarly to the model problem. Only one of the alternate problems will use the same knowledge components and be considered the match. Each of the alternate problems could match some of the surface features of the model problem.

===Research question===
Will robust learning be fostered if students are presented with relevant, annotated, worked-out examples instead of a targeted hint message when a learning event is encountered?

===Independent variables===
The manipulation in this study was based on the feedback the Andes system provided while participants worked on assigned homework problems covering Inductors:

;Examples Condition
:Participants were presented a relevant, annotated, worked-out example when help was requested.
;Hints Condition
:Participants were presented a targeted hint when help was requested.

===Hypothesis===
Providing annotated, worked-out examples instead of hints during problem solving will promote the learning of knowledge components and help appropriately generalize the knowledge components.

===Dependent variables & Results===
;Near Transfer, retention:
:Performance on problems involving inductors on the normal mid-term exam that were similar to the training problems. There was not significant difference in performance between the two conditions. Both conditions did better than a baseline of participants who solved no homework problems.
;Transfer task, deep structure assessment
:Problem matching task: No significant difference in performance between the two conditions; however, participants in the ''examples'' condition solved fewer training problems. Both conditions did better than a baseline of participants who solved no homework problems.
;Homework:
:Number of problems completed: Participants in the ''examples'' condition solved significantly fewer problems than participants in the ''hints'' condition.
:Time on task: Participants in the ''examples'' condition spent less time solving problems than those in the ''hints'' condition. Participants in both conditions spent about the same amount of time per problem.

===Explanation===

===Annotated bibliography===
* Ringenberg, Michael A. & VanLehn, Kurt (2006). ''Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning.'' Paper presented at the ITS 2006, Taiwan. Winner of Best Paper First Authored by a Student Award.

===References===

[[Category:Study]]