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== The Effects of Interaction on Robust Learning ==
 
== The Effects of Interaction on Robust Learning ==
 
  ''Robert Hausmann and Kurt VanLehn''
 
  ''Robert Hausmann and Kurt VanLehn''
 +
 +
=== Summary Table ===
 +
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
 +
| '''PIs''' || Robert G.M. Hausmann & Kurt VanLehn
 +
|-
 +
| '''Other Contributers''' || Robert N. Shelby (USNA), Brett van de Sande (Pitt)
 +
|-
 +
| '''Study Start Date''' || Sept. 1, 2006
 +
|-
 +
| '''Study End Date''' || Aug. 31, 2007
 +
|-
 +
| '''LearnLab Site''' || none (''in vitro'' study)
 +
|-
 +
| '''LearnLab Course''' || Physics
 +
|-
 +
| '''Number of Students''' || ''N'' = 39
 +
|-
 +
| '''Total Participant Hours''' || 78 hrs.
 +
|-
 +
| '''DataShop''' || Loaded: 11/02/07
 +
|}
 +
<br>
  
 
=== Abstract ===
 
=== Abstract ===
−
It is widely assumed that an interactive learning resource is more effective in producing learning gains than non-interactive sources. It turns out, however, that this assumption may not be completely accurate. For instance, research on human tutoring suggests that human tutoring (i.e., interactive) is just as effective as reading a textbook (i.e., non-interactive) under very particular circumstances (VanLehn et al., in press). This rises the question, under which conditions should we expect to observe strong learning gains from interactive learning situations? The current project seeks to address this question by contrasting interactive learning (i.e., jointly constructing explanations) with non-interactive learning (i.e., individually constructing explanations).
+
It is widely assumed that an interactive learning resource is more effective in producing learning gains than non-interactive [[sources]]. It turns out, however, that this assumption may not be completely accurate. For instance, research on human tutoring suggests that human tutoring (i.e., interactive) is just as effective as reading a textbook (i.e., non-interactive) under very particular circumstances (VanLehn et al., 2007). This raises the question, under which conditions should we expect to observe strong learning gains from interactive learning situations?  
 +
 
 +
The current project seeks to address this question by contrasting interactive learning (i.e., jointly constructing explanations) with non-interactive learning (i.e., individually constructing explanations). Students were prompted to either self-explain in the singleton condition or to jointly construct explanations in the dyad condition.
  
 
=== Background and Significance ===
 
=== Background and Significance ===
−
Several studies on collaborative learning have shown that it is more effective in producing learning gains than learning the same material alone. This finding has been replicated in many different configurations of students and across several different domains. Once the effect was established, the field moved into a more interesting phase, which was to accurately describe the interactions themselves and their impact on student learning (Dillenbourg, 1999). One of the hot topics in collaborative research is on the "co-construction" of new knowledge. Co-construction has been defined in many different ways. Therefore, the present study limits the scope of co-constructed ideas to jointly constructed '''explanations'''.  
+
Several studies on collaborative learning have shown that it is more effective in producing learning gains than learning the same material alone. This finding has been replicated in many different configurations of students and across several different domains. Once the effect was established, the field moved into a more interesting phase, which was to accurately describe the interactions themselves and their impact on student learning (Dillenbourg, 1999). One of the hot topics in collaborative research is on the "co-construction" of new knowledge. Co-construction has been defined in many different ways. Therefore, the present study limits the scope of co-constructed ideas to [[jointly constructed explanation]]s.  
  
−
Evidence of the impacton jointly constructed explanations is sparse, but can be found in a study by McGregor and Chi (2002). They found that collaborative peers are able to not only jointly constructed ideas, but they will also reuse the ideas in a later problem-solving session. One of the limitations of their study is that it did not assess impact of jointly constructed ideas on individual, robust learning. In a related study, Hausmann and Chi (2004) found correlational evidence for learning from co-construction. To provide more stringent evidence for the impact of jointly constructed explanations, the present sutdy will manipulate the types of conversations dyads have by promtping for jointly constructed explanations and measuring the effect on robust learning.  
+
Evidence supporting [[jointly constructed explanation]]s is sparse, but can be found in a study by McGregor and Chi (2002). They found that collaborative peers are able to not only jointly constructed ideas, but they will also reuse the ideas in a later problem-solving session. One of the limitations of their study was that it did not measure the impact of jointly constructed ideas on robust learning. In a related study, Hausmann, Chi, and Roy (2004) found correlational evidence for learning from co-construction. To provide more stringent evidence for the impact of jointly constructed explanations, the present study will manipulate the types of conversations dyads have by [[prompting]] for [[jointly constructed explanation]]s and measuring the effect on robust learning.
  
 
=== Glossary ===
 
=== Glossary ===
−
Jointly constructed explanation: a statement or set of statements, spread across two (or more) speakers, that makes explicit the causal or relational connection between concepts. Here is an example of a jointly constructed explanation in the domain of kinematics (from Hausmann & Chi, 2004):
+
* [[Jointly constructed explanation]]
 +
* [[Prompting]]
  
−
<p><tt>
+
See [[:Category:Hausmann_Study2|Hausmann_Study2 Glossary]]
  
−
{| class="wikitable" style="margin: 1em auto 1em auto"
+
=== Research question ===
−
! Jill:
+
How is [[robust learning]] affected by [[self-explanation]] vs. [[jointly constructed explanation]]s?
−
| Force acting on block B, is different from force acting on block A.
 
−
|-
 
−
! Sara:
 
−
| Ok. Because their mass, is different.
 
−
|-
 
−
! Jill:
 
−
| Yeah. Because-yeah.
 
−
|-
 
−
|}
 
−
</tt></p>
 
  
−
Jill presents a proposition (i.e., the forces are different) and Sara supplies the justification (i.e., ''because'' the masses are different).  
+
=== Independent variables ===
 +
Only one independent variable, with two levels, was used:
 +
* Explanation-construction: individually constructed explanations vs. jointly constructed explanations
  
−
Prompting: an explicit verbal reminder to engage in a specific interactive process, such as explaining. Here is an example of prompting for individual self-explanations (from Hausmann & Chi, 2002)
+
[[Prompting]] for an explanation was intended to increase the probability that the individual or dyad will traverse a useful learning-event path.
  
−
<p>
+
<b>Figure 1</b>. An example from the Individual Self-explanation condition<Br><Br>
−
<tt>
+
[[Image:Individual_SE.JPG]]<Br>
−
{| class="wikitable" style="margin: 1em auto 1em auto"
 
−
!Text:
 
−
|58. Renal circulation is the subsystem of systemic circulation that moves blood through the kidneys and back to the heart.
 
−
|-
 
−
!Student:
 
−
|ok, but why to the kidneys?
 
−
|-
 
−
!Tutor:
 
−
|Could you elaborate on what you just said?
 
−
|-
 
−
!Student:
 
−
|I don't understand why there's a system of the systemic circulation that directly relates to the kidneys
 
−
|-
 
−
!Tutor:
 
−
|Please click the "Next" button.
 
−
|}
 
−
</tt></p>
 
  
−
=== Research question ===
+
<b>Figure 2</b>. An example from the Joint Self-explanation condition<Br><Br>
−
How is robust learning affected by self-explanation vs. jointly constructed explanations?
+
[[Image:Joint_SE.JPG]]<Br>
  
−
=== Independent variables ===
+
=== Hypothesis ===
−
Two variables were crossed:
+
'''The Interactive Hypothesis''': collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Interactive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.
−
* Interaction: singleton vs. dyad
 
−
* Engagement: natural vs. prompted
 
−
 
 
−
The interaction variable manipulates whether the participants works individually (i.e., singleton) or with a collaborative peer (i.e., dyad).
 
  
−
Crossed with the first variable, the engagement variable manipulates if the participants are prompted to keep working together (i.e., natural) or reminds them to jointly construct an explanation of the step they just observed (i.e., prompted). Prompting for natural speech is to control for Hawthorn effects. Prompting for jointly construct explanations increases the probability that the dyad will traverse that particular learning event path.
+
This experiment implements the following instructional principle: [[Prompted Self-explanation]]
  
−
=== Hypothesis ===
+
=== Dependent variables ===
−
The Interactive Hypothesis: collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Intearctive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.  
+
* ''Near transfer, immediate'': electrodynamics problems solved in [[Andes]] during the laboratory period (2 hrs.).
  
−
The Coverage Hypothesis: if both peers and singletons cover the same knowledge components, then they will learn the same amount.
+
=== Results ===
 +
'''Laboratory Experiment'''
 +
We conducted a laboratory (''in vitro'' not ''in vivo'') experiment during the Spring 2007 semester. Undergraduate volunteers, who were enrolled in the second semester of physics at the University of Pittsburgh, composed the sample for the study. Unfortunately, the sample size was small because our pool of participants was extremely limited.
  
−
=== Dependent variables & Results ===
+
'''Procedure'''
−
* ''Near transfer, immediate'': electrodynamics problems solved in Andes during the laboratory period.
+
Participants were randomly assigned to condition. The first activity was to train the participants in their respective explanation activities. They read the instructions to the experiment, presented on a webpage, followed by the prompts used after each step of the example. Finally, they read an example of a hypothetical self-explanation or joint explanation.  
  
−
* ''Near transfer, retention'': homework preformance on electrodynamics problems that are isomorphic to the problems solved during the laboratory period.
+
After reading the experimental instructions, they then watched an introductory video to the Andes physics tutor. Afterwards, they solved a simple, single-principle electrodynamics problem (i.e., the warm-up problem). Once they finished, participants then watched a video solving an ''isomorphic problem''. Note that this procedure is slightly different from those used in the past where examples are presented before solving problems (e.g., Sweller & Cooper, 1985; Exper. 2). The videos decomposed into steps, and students were prompted to explain each step. The cycle of explaining examples and solving problems repeated until either 4 problems were solved or 2 hours elapsed. The first problem was used as a warm-up exercise, and the problems became progressively more complex.
  
−
* ''Far transfer, retention'': homework preformance on electrodynamics problems that are not isomorphic to the problems solved during the laboratory period.
+
As in our [[Hausmann_Study | first experiment]], we used normalize assistance scores. Normalize assistance scores were defined as the sum of all the errors and requests for help on that problem divided by the number of entries made in solving that problem. Thus, lower assistance scores indicate that the student derived a solution while making fewer mistakes and getting less help, and thus demonstrating better performance and understanding.
  
−
* ''Acceleration of future learning'': homework preformance on magnetisim problems.
+
The results from the laboratory study were as follows:  
  
−
<br>
+
* '''Differences between conditions'''
−
The data for this study have not been collected (target date = January, 2008). Our predicted results are:
 
  
−
'''Main Effect (Engagement):''' The coverage hypothesis (CI) makes the predictions shown in the Table below. First, there should be a main effect for interaction with dyads performing better than the singletons because, as the earlier studies on collaboration suggest, peer learning increases the probability of covering the set of knowledge applications compared to solo learning. The interaction hypothesis (IH) makes a similar prediction, but for a different reason. The interaction hypothesis states that the dyads will outperform the singletons because of the interaction itself.
+
The jointly constructed explanation (JCE) condition (''M'' = .45, ''SD'' = .33) demonstrated lower assistance scores than the individually constructed explanation (ICE) condition (''M'' = 1.00, ''SD'' = .67). The difference between experimental conditions was statistically reliable and of high practical significance, ''F''(1, 23) = 7.33, ''p'' = .01, η<sub>p</sub><sup>2</sup> = .24.
  
−
'''Main Effect (Prompting):''' The coverage hypothesis predicts a main effect for prompting such that the prompted condition will cover more knowledge components and thus learn more than the natural condition. Evidence for this prediction can be found in studies examining individual and collaborative learning. In terms of individual learning, Chi et al. (1994) successfully increased the rate of self-explanations through the use of content-free prompts, which then led to greater amounts of learning (especially on more difficult items). For collaborative learning, Coleman (1998) was able to prompt groups of students to produce explanations. Therefore, we expect that the prompting will raise the singletons up to the level of the dyads. The interaction hypothesis does not make a clear prediction for the effect of engagement. However, it would probably predict a null effect because the main effect of prompting combines or collapses over the beneficial effects of interaction; thus, washing out any effects of the prompting procedure.
+
* '''Problem by condition'''
  
−
'''Interaction (Prompting x Engagement):''' The coverage hypothesis predicts that the prompted-explanation dyads should learn the same amount as the prompted-explanation singletons. The interaction hypothesis would predict that the dyads should learn more than the singletons. This assumes that both groups cover the same knowledge applications, as intended by the prompting manipulation. However, if the solos have less prior knowledge at their disposal than the dyads, then they may not be able to cover as many knowledge applications as the dyads. In this case, both the coverage hypothesis and the interaction hypothesis predict that dyads will gain more. The manipulation check mentioned earlier will be used to determine whether the solos or dyads cover roughly the same number of knowledge components. The materials will be designed to make this as likely as possible.
+
The pattern observed at the level of conditions replicated at the level of problem. That is, when the problem was used as a within factor in a repeated measures analysis of variance (MANOVA), the JCE condition demonstrated lower normalized assistance scores for all of the problems, except for the first, warm-up problem (see Table).
  
 
<br>
 
<br>
 
{| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
 
{| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
−
| 
 
 
|
 
|
−
! colspan="2" | Interaction
+
|ICE<br>(n = 9)
−
|-  
+
|JCE<br>(n = 14)
−
|
+
|''p''
−
|
+
|η<sub>p</sub><sup>2</sup>
−
|Singletons
+
|-
−
|Dyads
+
|Warm-up ||0.75 ||0.63 ||.483 ||.024
 +
|-
 +
|Problem 2 || 1.09 || 0.32 ||.003 ||.341
 +
|-
 +
|Problem 3 || 1.08 || 0.51 ||.059 ||.160
 +
|-
 +
|Problem 4 || 0.67 || 0.29 ||.034 ||.196
 +
|}
 +
<br>
 +
 
 +
In addition to providing higher quality solutions, the jointly constructed explanation condition (''M'' = 985.71, ''SD'' = 45.60) also solved their problems more quickly than the individually constructed explanation condition (''M'' = 1097.75, ''SD'' = 51.45). Although the omnibus difference between experimental conditions was not statistically reliable, ''F''(1, 23) = 2.66, ''p'' = .12, η<sub>p</sub><sup>2</sup> = .10, the differences in solution times for the second and third problem were reliably lower for the JCE condition. This finding is particularly interesting because the experiment was capped at two hours; therefore, the dyads were able to complete the problem set more often than the individuals, χ<sup>2</sup> = 4.81, ''p'' = .03. There were no reliable differences in terms of the amount of time explaining the steps of the worked-out examples.
 +
 
 +
* '''Knowledge component (KC) by condition'''
 +
 
 +
Because not all of the individuals were able to complete the entire problem set, their data could not be included in an analysis of the knowledge components. A MANOVA assumes that each individual participates in all of the measures. However, this is not the case when the individuals did not complete the last problem. Therefore, this fine-grained analysis of learning will need to wait until the study can be replicated in the classroom, with a larger sample size.
 +
 
 +
In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.
 +
 
 +
<tt>
 +
{| border="1" cellspacing="0" cellpadding="5" align="center"
 +
|-
 +
|MAG || <center>[[Image:mag_learning_curves.JPG]]</center> || EFIELD || <center>[[Image:efield_learning_curves.JPG]]</center>
 
|-
 
|-
−
|rowspan="2"| Prompting
+
|FORCE || <center>[[Image:force_learning_curves.JPG]]</center> ||CHARGE || <center>[[Image:charge_learning_curves.JPG]]</center>
−
|Natural
 
−
| CH: Low Gain<br>IH: Low Gain
 
−
| CH: High Gain<br>IH: High Gain
 
 
|-
 
|-
−
|Explain
 
−
| CH: High Gain<br>IH: Low Gain
 
−
| CH: High Gain<br>IH: High Gain
 
 
|}
 
|}
 +
</tt>
 +
<center>'''Table 2'''. Learning curves for each knowledge component.</center>
 +
<br>
  
 
=== Explanation ===
 
=== Explanation ===
−
This study is part of the [[Interactive_Communication|Interactive Communication cluster]], and it hypothesizes that the '''Prompting''' manipulation should increase the probability that the students follow the deeper knowledge construction monolog or dialogs, whereas prompting for "natural" monologs or dialogs will most likely result in shallow statements about the learning material. Deeper knowledge construction monologs/dialogs will include intergrative statements that connect information with prior knowledge, connect information with or previously stated material, or infer new knowledge; whereas shallow monologs merely restate the presented material.  
+
This study is part of the [[Interactive_Communication|Interactive Communication cluster]], and it hypothesizes that the [[prompting]] singletons and dyads should increase the probability that they traverse useful [[learning events]]. However, it is unclear if the act of communicating with a partner should increase learning, if the type of statements (i.e., explanations) are held constant. A strong-sense version of the Interactive Communication hypothesis would suggest that interacting with a peer is beneficial for learning because dyads can learn from their partner by assimilating [[knowledge component]]s articulated by the partner or via corrective comments that help to refine vague or incorrect [[knowledge component]]s.
 +
 
 +
Both [[self-explanation]] and joint-explanation should lead to deeper [[Knowledge Construction Dialogues]] or monolgues because they are likely to include integrative statements that connect information with prior knowledge, connect information with or previously stated material, or infer new knowledge. However, it may be more likely that deeper knowledge construction occurs during dialogue because the communicative partner provides a social cue to avoid glossing over the material.
 +
 
 +
=== Further Information ===
 +
==== Annotated bibliography ====
 +
* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, July). Are self-explaining and coached problem solving more effective when done by pairs of students than alone? Poster presented at the 30th meeting of the Cognitive Science Society, Washington, DC.
 +
* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, June). Shall we explain? Augmenting Learning from Intelligent Tutoring Systems and Peer Collaboration. Paper presented at the 9th meeting of the International Conference on Intelligent Tutoring Systems, Montréal, Canada.
 +
* Hausmann, R. G. M., van de Sande, B., van de Sande, C., & VanLehn, K. (2008, June). Productive Dialog During Collaborative Problem Solving. Paper presented at the 2008 International Conference for the Learning Sciences, Utrecht, Netherlands.
 +
* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, May). Trialog: How Peer Collaboration Helps Remediate Errors in an ITS. Paper presented at the 21st meeting of the International FLAIRS Conference, Coconut Grove, FL.
 +
* Presentation to the PSLC Advisory Board: January, 2008
 +
* Conference proceeding submitted to CogSci 2008
 +
* Presented at a PSLC lunch: June 12, 2006 [http://www.learnlab.org/uploads/mypslc/talks/hausmannprojectproposalv4.ppt]
 +
* Presented to the Physics LearnLab: April 4, 2007
  
−
The '''Engagement''' manipulation should increase the likelihood that certain knowledge compontents are covered. That is, interactive dialogs (i.e., the jointly constructed explanation) should be more likely to cover knowledge components than non-interactive monologs (i.e., individual self-explanation).
+
==== References ====
 +
# Dillenbourg, P. (1999). What do you mean "collaborative learning"? In P. Dillenbourg (Ed.),  ''Collaborative learning: Cognitive and computational approaches'' (pp. 1-19). Oxford: Elsevier. [http://tecfa.unige.ch/tecfa/publicat/dil-papers-2/Dil.7.1.14.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., 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. [http://www.cogsci.northwestern.edu/cogsci2004/papers/paper445.pdf]
 +
# McGregor, M., & Chi, M. T. H. (2002). Collaborative interactions: The process of joint production and individual reuse of novel ideas. In W. D. Gray & C. D. Schunn (Eds.), ''24nd Annual Conference of the Cognitive Science Society.'' Mahwah, NJ: Lawerence Erlbaum. [http://www.cognitivesciencesociety.org/confproc/gmu02/final_ind_files/McGregor_chi.pdf]
 +
# 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://web.ebscohost.com/ehost/pdf?vid=2&hid=105&sid=34dbdad3-7874-419b-aace-3a9b56a3f41d%40sessionmgr107]
 +
# 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. [http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog3101_2]
  
−
=== Annotated bibliography ===
+
==== Connections ====
 +
* [[Craig observing | Learning from Problem Solving while Observing Worked Examples (Craig Gadgil, & Chi)]]
  
−
=== References ===
+
==== Future plans ====
−
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (in press). When are tutorial dialogues more effective than reading? ''Cognitive Science.''
+
Our future plans for June 2007 - August 2007:
 +
* Transcribe the interactions and code them for explanations, either constructed jointly or individually.

Latest revision as of 14:38, 10 October 2008

The Effects of Interaction on Robust Learning

Robert Hausmann and Kurt VanLehn

Summary Table

PIs Robert G.M. Hausmann & Kurt VanLehn
Other Contributers Robert N. Shelby (USNA), Brett van de Sande (Pitt)
Study Start Date Sept. 1, 2006
Study End Date Aug. 31, 2007
LearnLab Site none (in vitro study)
LearnLab Course Physics
Number of Students N = 39
Total Participant Hours 78 hrs.
DataShop Loaded: 11/02/07


Abstract

It is widely assumed that an interactive learning resource is more effective in producing learning gains than non-interactive sources. It turns out, however, that this assumption may not be completely accurate. For instance, research on human tutoring suggests that human tutoring (i.e., interactive) is just as effective as reading a textbook (i.e., non-interactive) under very particular circumstances (VanLehn et al., 2007). This raises the question, under which conditions should we expect to observe strong learning gains from interactive learning situations?

The current project seeks to address this question by contrasting interactive learning (i.e., jointly constructing explanations) with non-interactive learning (i.e., individually constructing explanations). Students were prompted to either self-explain in the singleton condition or to jointly construct explanations in the dyad condition.

Background and Significance

Several studies on collaborative learning have shown that it is more effective in producing learning gains than learning the same material alone. This finding has been replicated in many different configurations of students and across several different domains. Once the effect was established, the field moved into a more interesting phase, which was to accurately describe the interactions themselves and their impact on student learning (Dillenbourg, 1999). One of the hot topics in collaborative research is on the "co-construction" of new knowledge. Co-construction has been defined in many different ways. Therefore, the present study limits the scope of co-constructed ideas to jointly constructed explanations.

Evidence supporting jointly constructed explanations is sparse, but can be found in a study by McGregor and Chi (2002). They found that collaborative peers are able to not only jointly constructed ideas, but they will also reuse the ideas in a later problem-solving session. One of the limitations of their study was that it did not measure the impact of jointly constructed ideas on robust learning. In a related study, Hausmann, Chi, and Roy (2004) found correlational evidence for learning from co-construction. To provide more stringent evidence for the impact of jointly constructed explanations, the present study will manipulate the types of conversations dyads have by prompting for jointly constructed explanations and measuring the effect on robust learning.

Glossary

See Hausmann_Study2 Glossary

Research question

How is robust learning affected by self-explanation vs. jointly constructed explanations?

Independent variables

Only one independent variable, with two levels, was used:

  • Explanation-construction: individually constructed explanations vs. jointly constructed explanations

Prompting for an explanation was intended to increase the probability that the individual or dyad will traverse a useful learning-event path.

Figure 1. An example from the Individual Self-explanation condition

Individual SE.JPG

Figure 2. An example from the Joint Self-explanation condition

Joint SE.JPG

Hypothesis

The Interactive Hypothesis: collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Interactive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.

This experiment implements the following instructional principle: Prompted Self-explanation

Dependent variables

  • Near transfer, immediate: electrodynamics problems solved in Andes during the laboratory period (2 hrs.).

Results

Laboratory Experiment We conducted a laboratory (in vitro not in vivo) experiment during the Spring 2007 semester. Undergraduate volunteers, who were enrolled in the second semester of physics at the University of Pittsburgh, composed the sample for the study. Unfortunately, the sample size was small because our pool of participants was extremely limited.

Procedure Participants were randomly assigned to condition. The first activity was to train the participants in their respective explanation activities. They read the instructions to the experiment, presented on a webpage, followed by the prompts used after each step of the example. Finally, they read an example of a hypothetical self-explanation or joint explanation.

After reading the experimental instructions, they then watched an introductory video to the Andes physics tutor. Afterwards, they solved a simple, single-principle electrodynamics problem (i.e., the warm-up problem). Once they finished, participants then watched a video solving an isomorphic problem. Note that this procedure is slightly different from those used in the past where examples are presented before solving problems (e.g., Sweller & Cooper, 1985; Exper. 2). The videos decomposed into steps, and students were prompted to explain each step. The cycle of explaining examples and solving problems repeated until either 4 problems were solved or 2 hours elapsed. The first problem was used as a warm-up exercise, and the problems became progressively more complex.

As in our first experiment, we used normalize assistance scores. Normalize assistance scores were defined as the sum of all the errors and requests for help on that problem divided by the number of entries made in solving that problem. Thus, lower assistance scores indicate that the student derived a solution while making fewer mistakes and getting less help, and thus demonstrating better performance and understanding.

The results from the laboratory study were as follows:

  • Differences between conditions

The jointly constructed explanation (JCE) condition (M = .45, SD = .33) demonstrated lower assistance scores than the individually constructed explanation (ICE) condition (M = 1.00, SD = .67). The difference between experimental conditions was statistically reliable and of high practical significance, F(1, 23) = 7.33, p = .01, ηp2 = .24.

  • Problem by condition

The pattern observed at the level of conditions replicated at the level of problem. That is, when the problem was used as a within factor in a repeated measures analysis of variance (MANOVA), the JCE condition demonstrated lower normalized assistance scores for all of the problems, except for the first, warm-up problem (see Table).


ICE
(n = 9) 		JCE
(n = 14) 		p ηp2
Warm-up 0.75 0.63 .483 .024
Problem 2 1.09 0.32 .003 .341
Problem 3 1.08 0.51 .059 .160
Problem 4 0.67 0.29 .034 .196


In addition to providing higher quality solutions, the jointly constructed explanation condition (M = 985.71, SD = 45.60) also solved their problems more quickly than the individually constructed explanation condition (M = 1097.75, SD = 51.45). Although the omnibus difference between experimental conditions was not statistically reliable, F(1, 23) = 2.66, p = .12, ηp2 = .10, the differences in solution times for the second and third problem were reliably lower for the JCE condition. This finding is particularly interesting because the experiment was capped at two hours; therefore, the dyads were able to complete the problem set more often than the individuals, χ2 = 4.81, p = .03. There were no reliable differences in terms of the amount of time explaining the steps of the worked-out examples.

  • Knowledge component (KC) by condition

Because not all of the individuals were able to complete the entire problem set, their data could not be included in an analysis of the knowledge components. A MANOVA assumes that each individual participates in all of the measures. However, this is not the case when the individuals did not complete the last problem. Therefore, this fine-grained analysis of learning will need to wait until the study can be replicated in the classroom, with a larger sample size.

In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.

MAG
Mag learning curves.JPG
 EFIELD
Efield learning curves.JPG
FORCE
Force learning curves.JPG
 CHARGE
Charge learning curves.JPG

Table 2. Learning curves for each knowledge component.


Explanation

This study is part of the Interactive Communication cluster, and it hypothesizes that the prompting singletons and dyads should increase the probability that they traverse useful learning events. However, it is unclear if the act of communicating with a partner should increase learning, if the type of statements (i.e., explanations) are held constant. A strong-sense version of the Interactive Communication hypothesis would suggest that interacting with a peer is beneficial for learning because dyads can learn from their partner by assimilating knowledge components articulated by the partner or via corrective comments that help to refine vague or incorrect knowledge components.

Both self-explanation and joint-explanation should lead to deeper Knowledge Construction Dialogues or monolgues because they are likely to include integrative statements that connect information with prior knowledge, connect information with or previously stated material, or infer new knowledge. However, it may be more likely that deeper knowledge construction occurs during dialogue because the communicative partner provides a social cue to avoid glossing over the material.

Further Information

Annotated bibliography

  • Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, July). Are self-explaining and coached problem solving more effective when done by pairs of students than alone? Poster presented at the 30th meeting of the Cognitive Science Society, Washington, DC.
  • Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, June). Shall we explain? Augmenting Learning from Intelligent Tutoring Systems and Peer Collaboration. Paper presented at the 9th meeting of the International Conference on Intelligent Tutoring Systems, Montréal, Canada.
  • Hausmann, R. G. M., van de Sande, B., van de Sande, C., & VanLehn, K. (2008, June). Productive Dialog During Collaborative Problem Solving. Paper presented at the 2008 International Conference for the Learning Sciences, Utrecht, Netherlands.
  • Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, May). Trialog: How Peer Collaboration Helps Remediate Errors in an ITS. Paper presented at the 21st meeting of the International FLAIRS Conference, Coconut Grove, FL.
  • Presentation to the PSLC Advisory Board: January, 2008
  • Conference proceeding submitted to CogSci 2008
  • Presented at a PSLC lunch: June 12, 2006 [1]
  • Presented to the Physics LearnLab: April 4, 2007

References

  1. Dillenbourg, P. (1999). What do you mean "collaborative learning"? In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1-19). Oxford: Elsevier. [2]
  2. Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [3]
  3. Hausmann, R. G. M., 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. [4]
  4. McGregor, M., & Chi, M. T. H. (2002). Collaborative interactions: The process of joint production and individual reuse of novel ideas. In W. D. Gray & C. D. Schunn (Eds.), 24nd Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawerence Erlbaum. [5]
  5. 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. [6]
  6. 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. [7]

Connections

Future plans

Our future plans for June 2007 - August 2007:

  • Transcribe the interactions and code them for explanations, either constructed jointly or individually.
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