Difference between revisions of "Hausmann Diss"
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* Elaborative Dialog | * Elaborative Dialog | ||
* Critical Dialog | * Critical Dialog | ||
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=== Research question === | === 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? | ||
=== Independent variables === | === Independent variables === | ||
| + | * Collaboration training: elaboration vs. critical vs. control vs. individual | ||
=== Hypothesis === | === Hypothesis === | ||
| + | Elaborative dialog serves to increase the specification of another person’s message, which can then lead to efficient problem solving. | ||
=== Dependent variables === | === Dependent variables === | ||
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* ''Near transfer, retention'': | * ''Near transfer, retention'': | ||
| − | * ''Homework'': | + | * ''Homework'': |
| − | * ''Acceleration of future learning'': | + | * ''Acceleration of future learning'': for this laboratory study, there were no measures of accelerated future learning. |
=== Explanation === | === Explanation === | ||
Revision as of 13:50, 19 September 2006
Contents
Elaborative and critical dialog: Two potentially effective problem-solving and learning interactions
Robert G.M. Hausmann and Michelene T.H. Chi
Abstract
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.
Glossary
Forthcoming, but will probably include
- Elaborative Dialog
- Critical Dialog
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?
Independent variables
- Collaboration training: elaboration vs. critical vs. control vs. individual
Hypothesis
Elaborative dialog serves to increase the specification of another person’s message, which can then lead to efficient problem solving.
Dependent variables
- Near transfer, immediate:
- Near transfer, retention:
- Homework:
- Acceleration of future learning: for this laboratory study, there were no measures of accelerated future learning.
Explanation
Annotated bibliography
- Presentation to the NSF Site Visitors, May, 2005
- Presented at CogSci2006, July, 2006
References
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.
