Analyzing students' academic performance using educational data mining

Abstract

Educational Data Mining (EDM) is the process of extracting useful information and knowledge from educational data. EDM identifies patterns and trends from educational data, which can be used to improve academic curriculum, teaching and assessment methods, and students' academic performance. Thus, this study uses EDM techniques to analyze the performance of higher secondary students in Bangladesh. Three crucial categories, such as good, average, and poorly-performing students are considered for analysis. Four significant aspects of students' performance are emphasized for evaluation in this study. Firstly, predicting students' academic final examination performance in terms of internal college examination. Secondly, identifying all subjects' impact on classifier performance. Thirdly, examining students' performance progression during their studies and relating with subject-wise improvement or degradation. Fourthly, discovering consistent patterns of students' performance based on previous internal examination performance trends. The classification result reveals the correlation between internal examination and final academic performance. In addition, it resembles the predictor subjects for academic performance. The result also highlights the consistent pattern of students' consecutive internal examinations' performance. Thereafter, college administration can take necessary supportive initiatives for poorly-performing students and encourage good-performer students to continue excelling.

Keywords

• Educational data mining
• Classification
• Academic performance
• Performance progression
• Consistent performance