The field of Educational Data Mining (EDM) is still young and is focused on improving existing data mining (DM) techniques as well as creating new ones for locating data originating from educational systems. It seeks to employ these techniques to arrive at a logical understanding of students and the kind of learning environment they ought to experience. Knowledge Tracing (KT) and the prediction of student performance are closely intertwined. The academic community has made an effort to address it and has produced findings that are competitive. Several strategies have been implemented over the past 20 years that have improved on already-existing techniques by attacking the issue from different model architectures and experimenting with various datasets and formats. The efficiency of various machine learning models for predicting student performance is examined in this research. The outcomes were contrasted with earlier research that forecasted student achievement.

Educational Data Mining, Machine Learning, Student Performance Prediction, Knowledge Tracing, and Classification.

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