Evaluating a Bayesian Student Model of Decimal Misconceptions
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Evaluating a Bayesian Student Model of Decimal Misconceptions. / Goguadze, Giorgi; Sosnovsky, Sergey; Isotani, Seiji et al.
Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011). ed. / M. Pechenizkiy; T. Calders; C. Conati; S. Ventura; C. Romero; J. Stamper. Eindhoven University Press, 2011. p. 301-305 (EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Evaluating a Bayesian Student Model of Decimal Misconceptions
AU - Goguadze, Giorgi
AU - Sosnovsky, Sergey
AU - Isotani, Seiji
AU - McLaren, Bruce
N1 - Conference code: 4
PY - 2011
Y1 - 2011
N2 - applications of educational data mining, evaluation of student models is essential for an adaptive educational system. This paper describes the evaluation of a Bayesian model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an Intelligent Tutoring System within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of students' logged interactions from a study with 255 school children. Students' logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student's answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model's predictions reach a high level of precision, especially in predicting the presence of student misconceptions.
AB - applications of educational data mining, evaluation of student models is essential for an adaptive educational system. This paper describes the evaluation of a Bayesian model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an Intelligent Tutoring System within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of students' logged interactions from a study with 255 school children. Students' logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student's answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model's predictions reach a high level of precision, especially in predicting the presence of student misconceptions.
KW - Mathematics
KW - Bayesian networks
KW - Bayesian student modeling
KW - Student model evaluation
UR - http://www.scopus.com/inward/record.url?scp=84857480243&partnerID=8YFLogxK
M3 - Article in conference proceedings
SN - 978-90-386-2537-9
T3 - EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining
SP - 301
EP - 305
BT - Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011)
A2 - Pechenizkiy, M.
A2 - Calders, T.
A2 - Conati, C.
A2 - Ventura, S.
A2 - Romero, C.
A2 - Stamper, J.
PB - Eindhoven University Press
T2 - International Conference on Educational Data Mining - EDM 2011
Y2 - 6 July 2011 through 8 July 2011
ER -