Application of SimStudent for Error Analysis

Towards a theory 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.

Study Variables

Independent Variable

Prior knowledge: implemented as "operator" and "feature predicates" for SimStudent.

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.