Case study demonstrates predicting programming course performance, highlighting key factors influencing scores.
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.
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Mopuri et al. (2026) studied this question.
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