Early Detection of Under-Performing Students Using Machine Learning Algorithms

Abstract

Predicting student performance and identifying under-performing students early is the first step towards helping students who might have difficulties in meeting learning outcomes of a course resulting in a failing grade. Early detection in this context allows educators to provide appropriate interventions sooner for students facing challenges, which could lead to a higher possibility of success. Machine learning (ML) algorithms can be utilized to create an early warning system that detects students who need assistance and informs both educators and learners about their performance. In this paper, we explore the performance of different ML algorithms for identifying under-performing students in the early stages of an academic term/semester for a selected undergraduate course. First, we attempted to identify students who might fail their course, as a binary classification problem (pass or fail), with several experiments at different times during the semester. Next, we introduced an additional group of students who are at the borderline of failing, resulting in a multiclass classification problem. We were able to identify under-performing students early in the semester using only the first assessment in the course with an accuracy of 95%, and borderline students with an accuracy of 84%. In addition, we introduce a student performance prediction system that allows academics to create ML models and identify under-performing students early on during the academic term.