Evaluating a Bayesian Student Model of Decimal Misconceptions

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Educational Data Mining (EDM 2011)
EditorsM. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, J. Stamper
Number of pages5
PublisherEindhoven University Press
Publication date2011
ISBN (Print)978-90-386-2537-9
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference on Educational Data Mining - EDM 2011 - Eindhoven, Netherlands
Duration: 06.07.201108.07.2011
Conference number: 4

    Research areas

  • Mathematics
  • Bayesian networks, Bayesian student modeling, Student model evaluation