Difference between revisions of "Application of SimStudent for Error Analysis"
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===Abstract=== | ===Abstract=== | ||
| − | The purpose of this project is to study how students ''learn'' errors from examples. We apply a computational model of learning, called SimStudent | + | The purpose of this project is to study how students ''learn'' errors from examples. We apply a computational model of learning, called [http://www.cs.cmu.edu/~mazda/SimStudent SimStudent] that learns cognitive skills inductively from examples. In this study, we use SimStudent to mode a process of learning to study how and when erroneous skills (the skills that produce errors when applied) would be learned with the prior knowledge as a control variable. |
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| + | We are particularly interested in studying how the differences in prior knowledge affect the nature and rate of learning. We hypothesize that when students rely on shallow, domain general features (which we call "weak" features) as opposed to deep, more domain specific features ("strong" features), then students would more likely to make induction errors. | ||
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| + | To test this hypothesis, we provide SimStudent different set of prior knowledge and measure learning outcomes. | ||
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===Background and Significance=== | ===Background and Significance=== | ||
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===Hypothesis=== | ===Hypothesis=== | ||
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| + | ===Study Variables=== | ||
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| + | ====Independent Variable==== | ||
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| + | '''Prior knowledge''': implemented as "operator" and "feature predicates" for SimStudent. | ||
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| + | ====Dependent Variables==== | ||
===Findings=== | ===Findings=== | ||
Revision as of 20:16, 7 May 2009
An application of a computational model of learning as a model of learning errors
Noboru Matsuda, William W. Cohen, & Kenneth R. Koedinger
- PI: Noboru Matsuda
- Key Faculty: William W. Cohen, Kenneth R. Koedinger
Abstract
The purpose of this project is to study how students learn errors from examples. We apply a computational model of learning, called SimStudent that learns cognitive skills inductively from examples. In this study, we use SimStudent to mode a process of learning to study how and when erroneous skills (the skills that produce errors when applied) would be learned with the prior knowledge as a control variable.
We are particularly interested in studying how the differences in prior knowledge affect the nature and rate of learning. We hypothesize that when students rely on shallow, domain general features (which we call "weak" features) as opposed to deep, more domain specific features ("strong" features), then students would more likely to make induction errors.
To test this hypothesis, we provide SimStudent different set of prior knowledge and measure learning outcomes.
Background and Significance
Research Question
Hypothesis
Study Variables
Independent Variable
Prior knowledge: implemented as "operator" and "feature predicates" for SimStudent.
Dependent Variables
Findings
Impact of having "weak" prior knowledge in learning errors
Publications
- Matsuda, N., Lee, A., Cohen, W. W., & Koedinger, K. R. (2009; to appear). A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. In Conference of the Cognitive Science Society.
References
- Booth, J. L., & Koedinger, K. R. (2008). Key misconceptions in algebraic problem solving. In B. C. Love, K. McRae & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 571-576). Austin, TX: Cognitive Science Society.
