A Computational Model of How Learner Errors
Arise from Weak Prior Knowledge
Matsuda, N., Lee, A., Cohen, W. W., & Koedinger, K. R. (2009).
A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. In N. Taatgen & H. van Rijn (Eds.), Proceedings of the Annual Conference of the Cognitive Science Society (pp. 1288-1293). Austin, TX: Cognitive Science Society.
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 learning outcomes with the rate of learning, the accuracy of
learned skills (test scores), and the accuracy in predicting patterns of
real student errors. We found not only that the accuracy of learned skills
decreases when weak prior knowledge is given, but also the learning rate
significantly slows down. The accuracy of predicting student errors also
increased significantly, and 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).