A Systematic Literature Review of Student Performance Prediction Techniques in Virtual Learning Environment

Abstract

The virtual learning environment (VLE) is essential today and widely used globally for information exchange. Compared to in-person lectures, a VLE aids distant learning, although it might be challenging to maintain constant student interest. Academic activities are not actively pursued by students, which has an impact on their learning curves. The primary goal of this review is to impart a thorough knowledge and comprehension of various techniques, including machine learning (ML) and deep learning (DL), which are utilized for predicting student progress and performance and, consequently, how these prediction techniques help to find the most crucial student attribute for prediction. Additionally, this analysis reveals a rising trend in the volume and diversity of this field’s research. At the same time, the assessment revealed several problems with research quality that highlight the need for the community to strengthen efforts to validate and replicate work and to describe methods and outcomes in greater detail. It can help teachers, parents, students, and tutors decide on the appropriate learning support for their charges when taking online courses.