Hausmann Diss

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Elaborative and critical dialog: Two potentially effective problem-solving and learning interactions

Robert G.M. Hausmann and Michelene T.H. Chi


Recent research on peer dialog suggests that some dialog patterns are more strongly correlated with learning than others. A peer dialog, which is a subordinate category of interactive communication, occurs when two novices work together to collaboratively learn a set of knowledge components, solve a problem, or both. Two types of peer dialog that have been shown to be correlated with learning are elaboration and constructive criticism. Elaboration can be defined as a conditionally relevant contribution that significantly develops another person’s idea. Constructive criticism is defined as either a request for justification or an evaluation of an idea. The primary goal for this project was to move beyond correlating dialog patterns with outcomes by experimentally manipulating peer dialogs.

Participants were asked to solve a design problem, which was to optimize the design of a pre-existing bridge structure. Participants iteratively edited their design, analyzed its cost and effectiveness, and discussed their analyses to formulate their next modification. This process continued for thirty minutes, after which a posttest measuring both shallow and deep knowledge was administered.

The results indicated that the critical dyads generated the same number of critical statements as control dyads; therefore, the critical condition was collapsed into the control condition. Alternatively, the elaborative condition generated better designs and learned more deep knowledge than the control condition. The elaborations led to shorter negotiations about what design modification to try next, so more designs were tried. These students thus sampled more of the underlying design space. This may also explain their increased learning because more appropriate learning events occurred. The problem-solving and learning outcomes also suggest that training individuals to elaborate may have been easier than asking them to produce evaluative statements.

Background and Significance

Past research on collaborative problem solving and learning has painted a fairly consistent picture of both its costs and benefits. For instance, collaboration seems to be an effective educational intervention; however, not all collaborative dialogs lead to positive outcomes. Instead, only certain dialog patterns tend to result in strong learning gains. For example, generating explanations tends to be associated with understanding (Chi, Bassok, Lewis, Reimann, & Glaser, 1989) while paraphrasing does not (Hausmann & Chi, 2002). Therefore, one of the goals of the learning sciences is to identify dialog patterns that tend to produce strong learning gains (Dillenbourg, Baker, Blaye, & O'Malley, 1995). One such candidate is elaborative dialogs (Brown & Palincsar, 1989). Elaboration is a likely candidate because it has been shown to support individual learning. Why is it the case that elaboration is an effective problem solving interaction?


  • Elaborative interaction: a conditionally relevant contribution that significantly develops another person’s previously stated idea (Hogan, Nastasi, & Pressley, 1999).
  • Critical interaction: an interaction where the goal is to understand, evaluate, and improve the quality of an idea.

Research question

  • Can students be trained to collaborate in specific ways? If so, what is the effect of collaborative training on problem-solving performance and learning?
  • Why do elaborative dialogs lead to efficient problem solving and deep learning?
  • Do elaborative or critical interactions lead to better problem solving and/or learning than unscripted interactions?

Independent variables

  • Collaboration training: elaboration vs. critical vs. control vs. individual


The Interactive Communication cluster assumes that different types of interactions lead to different types of learning. Here, elaborative dialog is hypothesized to enhance learning and problem solving by increasing the specification of another person’s message.

To illustrate the hypothesis, consider an example from the domain in which this study was conducted: bridge design. Suppose one member of the dyad suggests they decrease the cross-sectional diameter of the members. The second member takes up this suggestion and extends it by proposing to only reduce the diameter of the vertical members. The verb "to change" requires the assignment of three variables, 1. an object to be modified, 2. the old property, and 3. the new property. Therefore, problem solving can be more efficient if the dyad elaborates the ideas by filling open variables. In contrast to elaboration, one member of the dyad could ask for clarification of the first member's idea (i.e., to request that the individual fill his or her own unfilled variable assignments). If the dyad is efficient, then they will be able to expose themselves to a wider array of knowledge components embedded in the simulated environment, thereby enhancing their learning from collaboration.

Dependent variables


  • Near transfer, immediate: Shallow learning was defined as the acquisition of information explicitly stated in the text.
  • Far transfer, immediate: Deep learning was defined as concepts that were not explicitly stated, and thus needed to be inferred from reading the text or interacting with the simulation.
  • Retension: For this laboratory study, there were no retension measures.
  • Acceleration of future learning: For this laboratory study, there were no measures of accelerated future learning.

Problem Solving

  • Iterations: the number of bridges tested.
  • Optimization score: the summation of the stress-to-strength ratios for each member, which was then divided by the total number of members:


where n = the number of members per bridge, stress is the amount of force loaded on the i-th member of the bridge, and strength is the maximum loading the i-th member can withstand before failure. The optimization score represents the average load per member; thus, higher values indicate better optimized designs

  • Savings score: the amount of money saved was calculated by subtracting the price of their final bridge from the starting price.


Problem solving

  • There was a marginal effect of condition on optimization score, F(2, 75) = 2.60, p = .08. Post-hoc analyses revealed a reliable difference between the elaborative dyads and control dyads, d = .61, but no difference between the individuals.
  • Elaborating a partner’s ideas and suggestions increased the dyads’ ability to optimize their designs.
  • On the other hand, there was no effect of condition on savings, suggesting all conditions constructed equally priced bridges, F(2, 75) < 1.


  • For shallow measures of learning, there were no differences between conditions F(2, 133) < 1.
  • For deep measures of learning, there was a main effect of condition, reflecting a higher score for the elaborative dyads (M = 11.18, SD = 18.81) than the control dyads (M = 3.31, SD = 15.33), F(2, 133) = 3.08, p < .05.

Communicative Differences

  • The elaborative dyads asked fewer clarification questions than the control condition.
  • The elaborative condition explicitly mentioned and used the simulation feedback more than the control condition.
  • The elaborative dyads were marginally more likely to be classified as using a positive strategy than the control dyads.


Elaboration may facilitate the rapid assignment of variables to unfilled slots. The results suggest that elaboration may have been effective in filling unassigned variables because the elaborative dyads asked fewer clarification questions than the control condition. Additionally, the results indicate that the elaborative dyads were better able to use the feedback from the simulation in deciding where to implement the changes.

Combining these two results, making faster variable assignments and better use of the simulation’s feedback, an exchange between two dyads in the elaboration condition might be interpreted in the following way. One person suggests that they modify the member properties by switching solid members to hollow. The second person may elaborate the suggestion by looking at the feedback and making a recommendation. If the second person makes explicit how she made her recommendation, then the use of the feedback is now available to the dyad for future use. The finding that the elaborative dyads were more likely to be classified as using positive strategies supports this interpretation. If the dyad is able to cover more of the design space, they will more likely be exposed to more knowledge components thereby learning deeper knowledge. Furthermore, the partner in the group can serve as an additional source of knowledge components. Members of a dyad are exposed to the knowledge embedded in the simulation, as well as the information shared by the partner.

Annotated bibliography

  • Presented at the Festschrift for Lauren Resnick, May, 2005
  • Presentation to the NSF Site Visitors, May, 2005
  • Presented at CogSci2006, July, 2006


Hausmann, R. G. M. (2006). Why do elaborative dialogs lead to effective problem solving and deep learning? In R. Sun & N. Miyake (Eds.), 28th Annual Meeting of the Cognitive Science Society (pp. 1465-1469). Vancouver, B.C.: Sheridan Printing. [1]

Hausmann, R. G. M. (2005). Elaborative and critical dialog: Two potentially effective problem-solving and learning interactions. Unpublished Dissertation, University Of Pittsburgh, Pittsburgh, PA. [2]