Predicting and Analysis of Students’ Academic Performance using Hybrid Techniques - M. AArul Rozario (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This paper presents a framework for predicting the academic performance of first-year bachelor’s students in computer science courses using data mining techniques. With the exponential growth of data in educational databases, data mining offers a promising avenue for uncovering valuable insights and patterns. The framework employs classification methods including Decision Tree, Naive Bayes, and Multi-Layer Perception, implemented through the python tool, to construct prediction models for students’ academic achievement. Experimental evaluations are conducted to determine the most effective model, with a focus on accuracy. Furthermore, the study emphasizes the significance of utilizing the extracted knowledge to profile students and assess their likelihood of success in the first semester. This research contributes to the field of educational data mining, offering insights that can potentially enhance student outcomes in computer science education.