The role of understanding in solving word problems
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
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..
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
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.
Similar books and articles
A. Danek, A. M. Hinz, F. Sürer, N. Kühnpast & A. H. Faber (2011). The Iso-Effect: Is There Specific Learning of Tower of London Iso-Problems? Thinking and Reasoning 15 (3):237-249.
A. H. Faber, N. Kühnpast, F. Sürer, A. M. Hinz & A. Danek (2009). The Iso-Effect: Is There Specific Learning of Tower of London Iso-Problems? Thinking and Reasoning 15 (3):237 – 249.
Maya Bar-Hillel (1989). How to Solve Probability Teasers. Philosophy of Science 56 (2):348-358.
Mareike B. Wieth & Rose T. Zacks (2011). Time of Day Effects on Problem Solving: When the Non-Optimal is Optimal. Thinking and Reasoning 17 (4):387 - 401.
Mark Burgin & Vladimir Kuznetsov (1994). Scientific Problems and Questions From a Logical Point of View. Synthese 100 (1):1 - 28.
Vanessa J. Clarke Koen Lamberts (1997). Strategy Shifts and Expertise in Solving Transformation Rule Problems. Thinking and Reasoning 3 (4):271 – 290.
Nicholas Maxwell (1980). Science, Reason, Knowledge, and Wisdom: A Critique of Specialism. Inquiry 23 (1):19 – 81.
Jonathan B. King (1993). Learning to Solve the Right Problems: The Case of Nuclear Power in America. [REVIEW] Journal of Business Ethics 12 (2):105 - 116.
Added to index2010-04-14
Total downloads246 ( #8,822 of 1,789,938 )
Recent downloads (6 months)26 ( #31,186 of 1,789,938 )
How can I increase my downloads?