Bayesian network analysis of academic performance and its behavioral determinants among junior high school students in Japan

Using Bayesian network analysis to analyze datasets from Japan’s National Assessment of Academic Ability, we examined how academic performance in junior high school students (aged 14 and 15) in Japan is predicted by students’ everyday and study behaviors. Academic performance is associated with study behaviors. Weekend study time, followed by study planning and weekday study time, had the greatest effect on academic performance. Multiple linear regression indicated that the effect of a standard weekend study on academic performance was 1.6 times greater than that of study planning and 3.4 times greater than that of weekday study time. Interventions to improve academic performance should adopt the following priorities. First, we should encourage students to spend time studying on weekends; this can be achieved by attending weekend private tuition and controlling weekend screen time. Second, we should encourage students to engage in study planning, which can be achieved by controlling weekday screen time, following a regular sleep schedule, and achieving their future ambitions and goals. Third, we should encourage students to spend weekdays studying out-of-school; this can be achieved by attending weekday private tuition and controlling social media time. The Bayesian network model suggests that improved sleep time may be associated with reduced screen time.