PREDICTING STUDENT PERFORMANCE IN PYTHON PROGRAMMING COURSE USING MULTIPLE LINEAR REGRESSION

This article reviews educational data mining and learning analytics approaches to predicting student performance in programming courses, with a case study applying multiple linear regression to Python programming students. The model explains 84% of score variability and highlights prior GPA, study hours, assignments, and attendance as dominant predictors.