An automatic prediction of students' performance to support the university education system: a deep learning approach

Predicting student performance is a critical aspect of educational systems. Although forecasting a student's future performance is essential in many applications, it is a challenging process due to various factors. Previous research in this area has mainly focused on comparing machine learning methods to automate student evaluation and predict their final performance. However, there have been limited studies that thoroughly explore the issue of class imbalance using a deep learning approach. Moreover, the large dataset targeting university students makes it well-suited for in-depth analysis and increases the likelihood of obtaining more accurate results. This study presents a deep learning model based on convolution and introduces a comprehensive exploration of oversampling and undersampling methods to address the issue of imbalanced classes. The paper investigates various features and characteristics of undergraduate students at the University of Jordan, utilizing a large dataset collected from the university's registration unit. These features include demographic information, attributes related to students' majors, faculties, registrations, courses taken (such as passed, repeated, and completed), as well as their high school averages and performance in the first four semesters. The results demonstrate that the model performs exceptionally well in terms of gmean when predicting students' excellence. This research project has significant implications and provides valuable insights to the research community and higher education managers, aiding in the development of improved strategies to enhance educational performance. Future researchers can utilize the methods employed in this paper during the data preprocessing stages and implement the demonstrated balancing strategies for further advancements in this field of study.