The ability to detect at-risk students in Virtual Learning Environment (VLE) is crucial for ensuring academic success. In this paper, we use machine learning methods and algorithms to identify students who may be struggling in a virtual learning setting. The study utilizes the Open University Learning Analytics Dataset (OULAD), which contains anonymized data about courses, students, and their interactions with VLE. This study aims to solve a binary classification problem, where the goal is to predict one of two possible outcomes – whether a student is at risk (1) or not at risk (0) of failing. The performance of the algorithms used is compared and evaluated, demonstrating their ability to effectively identify at-risk students in VLEs. This information can assist educators in addressing the needs of students who require additional support. Our findings highlight the potential of machine learning to positively impact student outcomes in virtual learning.
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