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)
Prior research has examined many potential predictors of student achievement. Evans and Simkin (1989) proposed 34 candidate variables, while Astin (1978) proposed 146. The reviewed factors include student demographics, educational background, psychological measures, prior knowledge, and formative-assessment results such as homework and assignments. Predictive approaches used in earlier studies include decision trees, naive Bayes, neural networks, support vector machines, and linear regression.
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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)