Data-Driven Student performance Modeling in Distance Learning Time: COVID-19 Era

Improving student performance is a top priority for academic institutions as it serves as a crucial indicator of their success or failure. Instructors and educational decision-makers heavily rely on predicting student performance to customize instruction based on individual needs. With the emergence of the COVID-19 pandemic, distance learning has become the primary method of education worldwide, making it essential for Higher Education Institutions to accurately predict student performance in this context. To achieve this, this research conducted a literature review to identify effective factors influencing student performance during distance learning. It also aimed to establish a suitable definition of student performance in this setting by exploring new concepts and attributes. Additionally, the research aimed to develop a prediction model for student performance during distance learning, incorporating the newly defined factors. The proposed data-driven model was empirically validated, and the results demonstrated the superiority of our approach, particularly in terms of the student performance definition and the machine learning-based technique used for prediction.