Analysis of AI- Empower Predictive Models for Predicting Student Performance in Higher Education

Source abstract

This research presents a study and review of previous research. It demonstrates the use of the most important techniques in predictive analytics and machine learning algorithms to analyze historical data and accurately predict future outcomes of student performance. This research focuses on specific objectives, including techniques used to identify students at risk of poor academic performance or dropout and enable timely interventions to improve outcomes. Moreover, in this paper, the answers to questions, such as their benefits and limitations. By using data from sources such as academic records, attendance, and engagement metrics, educational institutions can uncover patterns in student behavior and performance. The research also presents the most important findings that were reached. The results show that predictive analytics not only improves individual student performance but also enhances the effectiveness of the institution by promoting a supportive and proactive learning environment. This approach provides educators and educational institutions with actionable insights to effectively enhance student retention and enhance academic achievement.