Student Performance Prediction Using Educational Data Mining

Abstract - With the help of artificial intelligence (AI), education is going through a tremendous change. Through this change, it is now easier to get a better picture of how well students are doing and to spot kids who might quit school. This study looks at how learning analytics powered by artificial intelligence could be used to figure out how many students succeed and how many drop out by looking at patterns in students' attendance, online activity, and level of interest. The paper will use data from massive open online courses (MOOCs), online learning tools, and university databases to show how artificial intelligence can help students stay on track by allowing for timely interventions. It will be possible to do this by looking at the facts. That being said, it's possible that these treatments will include suggestions for adaptive learning and personalized feedback. The goal is to help teachers make smart choices, support students in doing well in school, and lower the number of students who drop out. This will be done with the help of an artificial intelligence model that can be trusted and is moral. To make sure that AI is used in schools in an acceptable way, this study will also look at problems like keeping data safe, getting rid of bias, and encouraging openness. At the end of the day, our study aims to make education more flexible, effective, and open to all students. Keywords: student performance prediction, dropout rate analysis, machine learning in education, predictive analytics, adaptive learning, student retention, early warning systems, personalized education, data-driven decision-making.