Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students identify at-risk early on. It answers some crucial intuitive questions are not addressed by previous studies. Most existing conducted on 2-3 years in an absolute grading scheme. We examined effects historical 15 predictive modeling. Additionally, we explore a relative scheme examine grades core courses initial semesters performances. As pilot study, analyzed Computer Science university students. Many exciting discoveries were made; duration size play significant role predicting performance, mainly due changes curriculum, faculty, society, evolving trends. Furthermore, advanced based pre-requisite challenging scheme, as students’ depends only their efforts but also peers. In short, educational come rescue uncovering insights critical areas need improvement.