Exploring Opportunities and Challenges of Machine Learning for Students in Higher Education: A Qualitative Perspective.

Abstract

The increasing complexity of higher education requires innovative approaches to improve student engagement and learning results. Conventional teaching approaches frequently lack real-time insights into student performance, resulting in ineffective interventions. This study presents a machine learning-based methodology for analysing both qualitative and quantitative student data, accurately predicting academic achievement. The suggested approach integrates deep learning and natural language processing, surpassing traditional techniques. The experimental findings indicate an accuracy of 94.5%, precision of 93.8%, recall of 94.2%, and an F1-score of 94.0%. The technology offers enhanced predictive capabilities compared to traditional models, delivering data-driven solutions for underperforming students. This research stands out itself from earlier studies by including qualitative insights with quantitative measures, hence enhancing the interpretability of learning analytics. The results encourage higher education institutions in creating AI-driven personalised learning paths, so enhancing retention rates and academic performance while addressing ethical issues in AI deployment.