Evaluating a bayesian student model of decimal misconceptions. 

Giorgi Goguadze, Sergey Sosnovsky, Bruce McLaren, S. Isotani

In: M. Pechenizkiy , T. Calders , C. Conati , S. Ventura , C. Romero , J. Stamper (Hrsg.). Proceedings of the 4th International Conference on Educational Data Mining. International Conference on Educational Data Mining (EDM) Eindhoven Netherlands Seiten 301-306 2011.


Among other 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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence