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

- Noboru Matsuda,
*Carnegie Mellon University*
- Andrew Lee ,
*Carnegie Mellon University*
- William W. Cohen ,
*Carnegie Mellon University*
- Kenneth Koedinger ,
*Carnegie Mellon University*

## 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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