Difference between revisions of "Application of SimStudent for Error Analysis"
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==Towards a theory of learning errors== | ==Towards a theory of learning errors== | ||
| − | + | ===Personnel=== | |
*PI: Noboru Matsuda | *PI: Noboru Matsuda | ||
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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 [http://www.cs.cmu.edu/~mazda/SimStudent SimStudent] that learns cognitive skills inductively from examples. In this study, we use 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 either from worked-out examples or by being tutored. In this study, we use SimStudent to study how and when erroneous skills (the skills that produce errors when applied) would be learned. |
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. | 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 | + | To test this hypothesis, we give SimStudent different sets of prior knowledge and analyze learning outcomes. |
| + | ===Background and Significance=== | ||
| + | |||
| + | A fundamental technology used for SimStudent is called Inductive Logic Programming (Muggleton, 1999) as an application for programming by demonstration (Cypher, 1993). Prior to learning, SimStudent is given a set of ''operators'' and ''feature predicates'' as prior knowledge. | ||
| − | |||
===Research Question=== | ===Research Question=== | ||
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*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. | *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. | ||
| + | |||
| + | *Muggleton, S. (1999). Inductive Logic Programming: Issues, results and the challenge of Learning Language in Logic. Artificial Intelligence, 114(1-2), 283-296. | ||
| + | |||
| + | *Cypher, A. (Ed.). (1993). Watch what I do: Programming by Demonstration. Cambridge, MA: MIT Press. | ||
Revision as of 16:26, 14 May 2009
Towards a theory of learning errors
Personnel
- 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 either from worked-out examples or by being tutored. In this study, we use SimStudent to study how and when erroneous skills (the skills that produce errors when applied) would be learned.
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 give SimStudent different sets of prior knowledge and analyze learning outcomes.
Background and Significance
A fundamental technology used for SimStudent is called Inductive Logic Programming (Muggleton, 1999) as an application for programming by demonstration (Cypher, 1993). Prior to learning, SimStudent is given a set of operators and feature predicates as prior knowledge.
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.
- Muggleton, S. (1999). Inductive Logic Programming: Issues, results and the challenge of Learning Language in Logic. Artificial Intelligence, 114(1-2), 283-296.
- Cypher, A. (Ed.). (1993). Watch what I do: Programming by Demonstration. Cambridge, MA: MIT Press.
