Students performance: From detection of failures and anomaly cases to the solutions-based mining algorithms

Abstract

Educational Data Mining (EDM) helps to recognise the performance of students and predict their academic achievements that include the successes aspects and failures, negative aspects and challenges. In the educational systems, a massive amount of students' data has been collected, which has become difficult for officials to search through and obtain the knowledge required to discover challenges facing students and universities by traditional methods. Therefore, the rooted problem is how to dive into these data and discover real challenges that are facing both the students and the universities. The main aim of this research is to extract hidden, significant patterns, new insights from students' historical data, which can solve the current problems, help to enhance the educational process and to improve academic performance. The data mining tools used for this task are classification, regression, and association rules for frequent patterns generation. The research data sets gathered from the College of Business and Economics (CBE). The finding of this research can help to make appropriate decisions for certain circumstances and provide better suggestions for overcoming students' weaknesses and failures. Through the findings, numerous problems related to a students' performance discovered at different levels and in various courses. The research findings indicated that there are many important problems. Consequently, a suggestion of suitable solutions, which can be presented to the relevant authorities for the benefit and improving student performance and activating academic advising.