A Prediction Model for Online Student Academic Performance Using Machine Learning

Abstract

Academic data mining impacts a large number of educational institutions, significantly, playing a prime role in accumulating, studying, and analyzing the academic data. The accumulated academic data can be processed and analyzed for various purposes such as predicting the student academic performance and broadening the retention rate. The prediction of students’ academic performance at the initial stage helps the students to identify their lacking subjects. Accordingly, the predicted results can assist the students to focus more on their deficient subjects so as to improvise their academic performance. Currently, numerous machine learning techniques are being used by the academic institutions to extract and analyze the valuable information ranging from predicting students’ academic performance to the identification of fast and slow learners. This paper intends to propose an ensemble model, using the voting method for preclusive prediction of the student academic performance. The predicted results are being further utilized by the poor performers to concentrate more on their deficit courses. Hence, the instructors can focus on creating and implementing novel strategies or amending the existing pedagogical tools and approaches to aid the slow learners in improvising their performance. The proposed model has been tested on the academic data of an educational institution using the RapidMiner tool.