Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning †

Abstract

The development of a knowledge- and information-based society can be aided by higher education. Through research and extension efforts, higher education institutions must perform a variety of functions, including building an intelligent human resource pool, gaining new skills, and creating new knowledge. As a result, the development of skilled workers with the ability to think critically, creatively, and logically is the primary focus of higher education institutions. However, there are some significant obstacles in the way of offering quality education, such as how to identify low-performing students and their causes. Predicting student performance has become challenging as a result of the vast quantity of data in educational databases. The lack of a developed system for assessing and monitoring student achievement is also not being considered. There are primarily two causes for this kind of situation. Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. The second is the lack of investigation into the courses. In this research work, efforts have been made to identify low-performing students through the proposed Back Propagation Neural Network for Student Performance Analysis (BPNN-SPA) model, which generates more accurate, efficient, and dependable results as compared to some of the existing techniques and models. The performance of the proposed model is compared with the Support Vector Machine and Random Decision algorithms and evaluated by four significant performance metrics, namely, sensitivity, specificity, accuracy, and the F-measure. Based on performance measures, the proposed BPNN-SPA achieved better accuracy than existing algorithms.