Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.