| Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms. | |
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MedLine Citation:
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PMID: 21820455 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. |
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Authors:
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John R Anderson |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-7-27 |
Journal Detail:
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Title: Neuropsychologia Volume: - ISSN: 1873-3514 ISO Abbreviation: - Publication Date: 2011 Jul |
Date Detail:
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Created Date: 2011-8-8 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0020713 Medline TA: Neuropsychologia Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011. Published by Elsevier Ltd. |
Affiliation:
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Department of Psychology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, United States. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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