David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Word problems are notoriously difficult to solve. We suggest that much of the difficulty children experience with word problems can be attributed to difficulty in comprehending abstract or ambiguous language. We tested this hypothesis by (1) requiring children to recall problems either before or after solving them, (2) requiring them to generate f'mal questions to incomplete word problems, and (3) modeling performance pattems using a computer simulation. Solution performance was found to be systematically related to recall and question generation performance. Correct solutions were associated with accurate recall of the problem structure and with appropriate question generation. Solution "errors" were found to be correct solutions to miscomprehended problems. Word problems that contained abstract or ambiguous language tended to be miscomprehended more often than those using simpler language, and there was a great deal of systematicity in the way these problems were miscomprehended. Solution error pattems were successfully simulated by manipulating a computer model’s language comprehension strategies, as opposed to its knowledge of logical set relations. o was..
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Suzanne M. Mannes & Walter Kintsch (1991). Routine Computing Tasks: Planning as Understanding. Cognitive Science 15 (3):305-342.
Mark D. LeBlanc & Sylvia Weber‐Russell (1996). Text Integration and Mathematical Connections: A Computer Model of Arithmetic Word Problem Solving. Cognitive Science 20 (3):357-407.
Daniel L. Schwartz & Joyce L. Moore (1998). On the Role of Mathematics in Explaining the Material World: Mental Models for Proportional Reasoning. Cognitive Science 22 (4):471-516.
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