A BAYESIAN APPROACH IN STUDENTS' PERFORMANCE ANALYSIS

Psychological and environmental aspects have a great influence on students' performance in higher education. Analysing and modelling the performance of students helps educational institutes to improve the quality of their course offering. Furthermore, it helps students to perform better in their studies and therefore has a better competence. Bayesian networks can model the causal interactions and statistical relationships of a system's variables with a graphical demonstration, which is easy to interpret. Using such a model and having evidence about one or many of performance indicators of students, it is possible to investigate the status of other indicators in the model. It is also possible to predict the intervention impact on one or more indicators on the other parts of the network. Development of machine learning techniques for Bayesian networks in the recent years makes it possible to discover the knowledge of a domain automatically using the collected data. In this study, the Bayesian networks are used to model the causal relation of student's performance factors and then the model is used to classify the students according to performance and explore the effect of the intervention on each individual student. The results indicate that by reducing the fear-of-failure factor by 30 percent will impact the overall students' performance and reduce the academic withdrawal factor.