A Bayesian Network for the Cognitive Diagnosis of Deductive Reasoning
In our previous works, we presented Logic-Muse as an ITS that helps improve logical reasoning skills in multiple contexts. All its three main components (the learner, tutor and expert models) have been developed while relying on the help of experts and on important work in the field of reasoning and computer science. The main purpose of this paper is to present and assess the Bayesian Network (that allows real time diagnosis and modeling of the learner’s state of knowledge) implemented in the learner component. We demonstrate the prediction and the adaptive capabilities for our learner model by using data mining techniques on data from 71 students. We believe this work will help the research community in building and assessing a BN in an ITS that teach logical reasoning.
KeywordsBayesian network Deductive reasoning Learner model Intelligent tutoring system
- 6.Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., Tato, A.: Towards an intelligent tutoring system for logical reasoning in multiple contexts. In: Conole, G., Klobucar, T., Rensing, C., Konert, J., Lavoué, E. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 460–466. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24258-3_40 Google Scholar
- 7.Robitzsch, A., et al.: CDM: Cognitive diagnosis modeling. R Package version, 3 (2014)Google Scholar