Predicting academic performance of students is always an interesting area of research for academicians. Different inputs were considered to predict the academic performance of students in several research publications. If predictions are made too early accuracy of results will be affected as the performance of the students depend upon several factors during the semester the student is studying. We are postponing the predictions until the middle of the semester to have better monitoring and control of the students for the final grades. We use key formative assessment methods to predict the performance of the students at the end of the semester. We used popular machine learning classification methods Naïve Bayes, Random Forest and Support Vector Machine (SVM) to predict the end of the semester performance.
Key words: Grade Prediction, Machine Learning, Naïve Bayes, Random Forest, Support Vector Machine
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