Hybrid Data Science Approaches to Predict the Academic Performance of Students

Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize the strengths and weaknesses of their academic programs and thereby take the necessary corrective interventions to ameliorate student achievements. To this end, our paper contributes, to begin with, a hybrid approach of factor analysis that combines various data science approaches. The prediction of student performance is produced by combining baseline models, cross-validation, and factor analysis. We empirically investigate and demonstrate the effectiveness of our entire approach on four datasets. The experimental results show considerable improvements compared to single baseline models, demonstrating the practicality of the proposed approach in pinpointing multiple factors impacting student performance. The result proves that the hybrid algorithm combining cross-validation and factor analysis approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students. The model may be successfully extended to other programs to predict the performance of the students.