A Computational Model of How Learner Errors Arise from Weak Prior Knowledge

Abstract

How do differences in prior conceptual knowledge affect the nature and rate of learning? To answer this question, we built a computational model of learning, called SimStudent, and conducted a controlled simulation study to investigate how learning a complex skill changes when the system is given “weak” domain-general vs. “strong” domain-specific prior knowledge. We measured SimStudent’s learning outcomes as the rate of learning, the accuracy of learned skills (test scores), and the fit to the pattern of errors made by real students. We found that when the “weak” prior knowledge is given, not only the accuracy of learned skills decreases, but also the learning rate significantly slows down. The accuracy of predicting student errors increased significantly – namely, SimStudent with the weak prior knowledge made the same errors that real students commonly make. These modeling results help explain empirical results connecting prior knowledge and student learning (Booth & Koedinger, 2008).


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