Prediction of Student's Performance With Learning Coefficients Using Regression Based Machine Learning Models

Advanced machine learning (ML) methods can predict student's performance with key features based on academic, behavioral, and demographic data. Significant works have predicted the student's performance based on the primary and secondary data sets derived from the student's existing data. These works have accurately predicted student's performance but did not provide the metrics as suggestions for improved performance. This paper proposes the 'Learning Coefficients' evaluated through trajectory-based computerized adaptive assessment. Learning coefficients also provide quantified metrics to the students to focus more on their studies and improve their further performance. Before selecting the learning coefficients as the key features for student's performance prediction, their dependency on other key features is calculated through positive Pearson's coefficient correlation. Further, the paper presents comparative analysis of the performance of regression-based ML models such as decision trees, random forest, support vector regression, linear regression and artificial neural networks on the same dataset. Results show that linear regression obtained the highest accuracy of 97% when compared to other models.