Using Machine Learning To Predict Student Performance In Online Learning Environments

Abstract

Prediction of academic performance of students is one of the major topics for universities and
schools as it can be helpful to design the right mechanisms to avoid dropouts and improve
academic results, among others. A lot of processes have been automated in usual activities of
students to benefit them and manage big data gathered from software products for tech-based
learning. Hence, processing and analyzing the same data properly can give a lot of vital insights
to their knowledge and relation between students and their homework. This information can feed
promising methods and algorithms for prediction of student performance.
This study is conducted to analyze various machine learning models used for predicting student’s
performance. This study presents an in-depth review of studies examining data of online learning
environments to predict students’ outcomes with machine learning techniques. This study will help
identify the online course features used for prediction of learners’ outcome, determine the outputs
of prediction, strategies, and methodologies of feature extraction for prediction of performance,
evaluation metrics, and challenges and limitations for analyzing the outcomes.