Examining the factors affecting students` science success with Bayesian networks

Authors : Hasan Aykut KARABOĞA, İbrahim DEMİR

Pages : 413-433

Doi:10.21449/ijate.1218659

View : 72 | Download : 111

Publication Date : 2023-09-22

Article Type : Research Paper

Abstract :Bayesian Networks insert ignore into journalissuearticles values(BNs); are probabilistic graphical statistical models that have been widely used in many fields over the last decade. This method, which can also be used for educational data mining insert ignore into journalissuearticles values(EDM); purposes, is a fairly new method in education literature. This study models students\` science success using the BN approach. Science is one of the core areas in the PISA exam. To this end, we used the data set including the most successful 25% and the least successful 25% students from Turkey based on their scores from Program for International Student Assessment insert ignore into journalissuearticles values(PISA); survey. We also made the feature selection to determine the most effective variables on success. The accuracy value of the BN model created with the variables determined by the feature selection is 86.2%. We classified effective variables on success into three categories; individual, family-related and school-related. Based on the analysis, we found that family-related variables are very effective in science success, and gender is not a discriminant variable in this success. In addition, this is the first study in the literature on the evaluation of complex data made with the BN model. In this respect, it serves as a guide in the evaluation of international exams and in the use of the data obtained.
Keywords : Bayesian Networks, Educational Data Mining, PISA, Science Success