Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information

ABSTRACT: Over the past few years, online learning has exploded in popularity due to the potentially unlimited enrollment, lack of geographical limitations, and free accessibility of many courses. However, learners are prone to have poor performance due to the unconstrained learning environment, lack of academic pressure, and low interactivity. Personalized intervention design with the learners’ background and learning behavior factors in mind may improve the learners’ performance. Causality strictly distinguishes cause from outcome factors and plays an irreplaceable role in designing guiding interventions. The goal of this paper is to construct a Bayesian network to make causal analysis and then provide personalized interventions for different learners to improve learning. This paper first construc

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PROVIDER: S-EPMC7514446 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature