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Evaluating Learning Factors analysis

Lipschultz, M and Litman, D and Jordan, P and Katz, S (2012) Evaluating Learning Factors analysis. In: UNSPECIFIED UNSPECIFIED. ISBN UNSPECIFIED

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Abstract

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 cross-validated 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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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lipschultz, Mmil28@pitt.eduMIL28
Litman, Ddlitman@pitt.eduDLITMAN
Jordan, Ppjordan@pitt.eduPJORDAN
Katz, Skatz@pitt.eduKATZ
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 December 2012
Date Type: Publication
Journal or Publication Title: CEUR Workshop Proceedings
Volume: 872
Event Type: Conference
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISSN: 1613-0073
Date Deposited: 22 Sep 2014 19:51
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/22885

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