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 -