Steps towards a predictive semantic resource recommendation system for university students (2020)
Using information from the University of Los Andes, we created student performance prediction models for each compulsory course of three different programs: Industrial Engineering, Economy, and Systems and Computing Engineering. We created 3 feature sets using a wide range of academic metrics. Using 6 classification models we predicted whether a student would get a grade above the course average or below the course average, achieving a balanced accuracy of up to 77.8%. Using 7 regression algorithms we predicted a student's final grade (numerical value between 0 and 5) on a given course, achieving a minimum MAE of 0.234 and a R squared score of up to 46.4%. Results from the machine learning models indicated that the Random Forest algorithm was the best when predicting a course's grade using previous course grades. Using the Random Forest models, we analyzed the feature importance of certain courses when predicting other courses...