Evaluating a bayesian student model of decimal misconceptions. 

Giorgi Goguadze; Sergey Sosnovsky; BruceM. 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, Pages 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