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)
Lots of factors can have an impact on student achievement. Different studies provided distinctive guidelines for the features that can be used for this purpose. (Evans & Simkin, 1989) proposed 34 such variables, while (Astin, 1978) 146. Additionally, student demographic, educational background, psychological data can be useful but they have more complex relation regarding academic progress data. Several studies have proved that prior knowledge is an important variable. Formative assessment tasks can also be useful for predicting student performance. Such tasks include homework, assignments. Different predictive data mining models have been used to predict student performances, that include, Decision Tree, Naive Bayes, Neural Networks, SVMs, linear regression and etc.
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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)
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