A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning Techniques

For scholarly organizations, students’ academic performance (AP) computes student achievements in different academic subjects. Therefore, a systematic literature review based on machine learning approaches to improve student performance is proposed. This field creates a way to discover hidden examples from instructive information. Machine learning (ML) techniques are used for performance prediction. It is become a challenge due to its imbalanced dataset. This review aims to identify the best proposals that focus on various ML methods used for the performance analysis of students. This review will become helpful to the teachers in identifying the weak students, and it helps to improve their performance through proper guidance. Thus, it reflects in the student’s background and boosts their growth. It also brings benefits to students, teachers, and institutions. This study focused on applying machine learning techniques to predict students’ performance in recent times. With a systematic approach, the research identified the existing prediction methods and tools used to predict students’ performance and observed the researchers’ type of variables in this research area. In this study, almost more than 100 papers were analyzed to reveal various modern techniques that are commonly used to predict student performance and the goals they need to achieve in this field. The results from the various research will help improve students’ academics and monitor the student’s performance, which would also improve their literacy rate.