Modeling of Learning Process based on Bayesian Networks

Abstract

While f ields related to educational data analytics such as learning analytics are rapidly developing, the importance of institutional research is recognized from the viewpoint of assuring the quality of education and management assistance. To undertake organizational analysis and provide support that is tailored to individuals, the integration of these f ields will become increasingly important. In this study, as a framework for utilizing a method of learning analytics for institutional research and student support, we investigated a method for modeling the transition process of students’ learning states by using Bayesian networks. Its applicability was investigated based on the results of numerical simulations conducted as a part of the study.