Evaluating learning factors analysis

نویسندگان

  • Michael Lipschultz
  • Diane J. Litman
  • Pamela W. Jordan
  • Sandra Katz
چکیده

Learning Factors Analysis (LFA), a form of student modeling, is used to predict whether a student can correctly answer a tutor question. Existing evaluations of LFA rely on metrics like the crossvalidated root mean squared error (RMSE). However, the LFA output can be a binary classification (the student will be correct or not), so we can use classification metrics, such as precision and recall, to evaluate LFA models. In this paper, we show that this finer-grained analysis can lead to different conclusions than relying on only RMSE.

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تاریخ انتشار 2012