Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model
This study develops and validates a Support Vector Machine (SVM) regression model to predict final exam performance and identify at-risk students in an introductory programming course. Using data from 190 novice students on the ViLLE platform (2012-2016)—including homework assignments, demo exams, and prior programming knowledge—the model found these factors correlated with final grades. However, the SVM model achieved a low prediction accuracy of 52% overall and 46.4% for at-risk students, which the authors attributed to multicollinearity and the variables' inability to capture individual characteristics.
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Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model
Learn After
Introduction (Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model)
Literature Review (Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model)
Research Method (Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model)
Data Analysis and the Results (Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model)
Discussion (Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model)