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)
As the authors found, homework assignments, demo exams, and prior programming knowledge are correlated with the final exam grade. However, these variables do not capture individual characteristics, which may explain the model's low accuracy of 52%. The authors also explain that the model has a multicollinearity issue, and a lack of features may be another reason for its low performance. For predicting students who are at risk, performance was even lower (46 4).
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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)