Exploring Correlated Features in Deep Learning Models for Academic Advising

Abstract

In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and personalized interaction, which are not scalable. Machine learning and deep learning have the potential to completely transform advising by automating parts of process and giving students distinctive guidance. This paper explores the relationship between student academic performance and correlated features using deep learning methodology in academic advising systems. The model was trained and tested with the Kaggle dataset, and the results show high accuracy, specificity, and true positive rate values compared to naive bayes and random forest methodologies. The paper also addresses potential challenges and limitations of deep learning in academic advising, such as data privacy concerns, algorithmic bias, and the need for human intervention. It concludes by emphasizing the importance of incorporating ethical considerations and human expertise in the development and deployment of deep learning models in academic advising. Overall, the paper highlights the potential of deep learning algorithms to transform academic advising and improve student success.