Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students' abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study's findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study's results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance.