Process and Outcome Studies of Representational Guidance for Scientific Inquiry

External (visual and textual) representations differ in the information they are capable of capturing, in the ease of recovering information that is captured, and in the ease of identifying information that is missing. Consequently, representations may differ in how they guide learning processes. We call these differences between representations "representational guidance." Effects of representational guidance on individual problem solving are well documented, yet there is a lack of comparative research on how representational effects extend to collaborative learning situations. This paper introduces the concept of representational guidance for collaborative learning discourse and summarized the results of two studies: a laboratory study investigating differences in discourse processes, and a classroom study investigating differences in products of the investigations. The laboratory study compared three representational tools for recording and reasoning about evidence during collaborative inquiry. The tools were unconstrained text (a word processor), graphical evidence mapping and a matrix or tabular representation. Participants were provided with hypertext-based materials pertinent to a problem in which multiple explanations were proposed for a disease. Analysis of discourse transcripts was consistent with predictions concerning the amount of talk about evidential relations. The classroom study focused on the effects of representational tools and of assessment rubrics on learning to evaluate diverse sources of empirical evidence and coordinate these data with domain theories. The representational tools were graphical evidence maps and text. The assessment rubrics provided specific criteria for scientific inquiry, encouraging self-reflection and peer coaching. The results indicate that evidence mapping provides better support than prose, as evidenced by amount of information and quality of inferences recorded in classroom artifacts. The assessment rubrics improved the quality of work of students using the evidence mapping tool but not those using prose.

[1]  D. Suthers,et al.  Mapping to know: The effects of evidence maps and reflective assessment on scientific inquiry skills. , 2002 .

[2]  F. R. A. Hopgood,et al.  Machine Intelligence 3 , 1969, The Mathematical Gazette.

[3]  J. Flavell Metacognitive aspects of problem solving , 1976 .

[4]  Joseph D. Novak,et al.  Learning How to Learn , 1984 .

[5]  Saul Amarel,et al.  On representations of problems of reasoning about actions , 1968 .

[6]  Mark Guzdial,et al.  Integrating and Guiding Collaboration: Lessons learned in computer-supported collaboration learning , 1997 .

[7]  Susanne P. Lajoie,et al.  Computers As Cognitive Tools , 2020 .

[8]  Daniel D. Suthers,et al.  Representational support for collaborative inquiry , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[9]  James H. Wandersee,et al.  Concept Mapping and the Cartography of Cognition. , 1990 .

[10]  Herbert A. Simon,et al.  Collaborative Discovery in a Scientific Domain , 1997, Cogn. Sci..

[11]  Jon Oberlander,et al.  A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation , 1995, Cogn. Sci..

[12]  J. Roschelle Designing for cognitive communication: epistemic fidelity or mediating collaborative inquiry? , 1997, Computers, Communication and Mental Models.

[13]  A. Collins,et al.  Epistemic forms and Epistemic Games: Structures and Strategies to Guide Inquiry , 1993 .

[14]  Jiajie Zhang,et al.  The Nature of External Representations in Problem Solving , 1997, Cogn. Sci..

[15]  Daniel D. Suthers,et al.  An integrated approach to implementing collaborative inquiry in the classroom , 1997, CSCL.

[16]  L. R. Novick,et al.  Transferring symbolic representations across nonisomorphic problems. , 1994 .

[17]  D. Klahr,et al.  Formal assessment of problem-solving and planning processes in preschool children , 1981, Cognitive Psychology.

[18]  Philip Bell,et al.  Using argument representations to make thinking visible for individuals and groups , 1997, CSCL.

[19]  D. Perkins Person plus: A distributed view of thinking and learning , 1994 .

[20]  Ryszard S. Michalski,et al.  Machine learning: an artificial intelligence approach volume III , 1990 .

[21]  H. Simon,et al.  What makes some problems really hard: Explorations in the problem space of difficulty , 1990, Cognitive Psychology.

[22]  John R. Hayes,et al.  The Complete Problem Solver , 1981 .

[23]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[24]  J. Novak Concept mapping: A useful tool for science education , 1990 .

[25]  Jim Hewitt Design Principles for the Support of Distributed Processes , 1996 .

[26]  Paul E. Utgoff,et al.  Shift of bias for inductive concept learning , 1984 .

[27]  G. Salomon Distributed cognitions : psychological and educational considerations , 1997 .

[28]  Daniel D. Suthers,et al.  Learning by Constructing Collaborative Representations: An Empirical Comparison of Three Alternatives. , 2001 .