A Review on Student Performance Prediction Based on Machine Learning Techniques

Educational Organizations have lots of student-related data. This may be beneficial for predicting student-related activities. Mining such academic-related data will be helpful for academic organizations to comprehend the performance of various stakeholders. Educational Data Mining, is a prominent area that explores various frameworks and approaches to handle data related to education. Predicting student performance is one crucial aspect where EDM can play a significant role. This paper presents data collected from popular data sources through the review of relevant papers. This review primarily focuses on three major areas Machine Learning Techniques, Student Attributes, and Feature Selection Techniques. The study explores factors relevant to Student Success Prediction and assesses the effectiveness of different machine learning techniques applied to enhance predictive models for student success.