Modeling Techniques (Predicting student performance using data from an Auto-grading system)
The paper 'Predicting student performance using data from an Auto-grading system' evaluates two modeling techniques: decision trees and linear regression. Decision trees are built from the training set by choosing, at each split, the feature that yields the maximum possible information gain. For the regression approach, the authors use multivariate linear regression because the model includes several independent variables (features).
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Introduction (Predicting student performance using data from an Auto-grading system)
Related Work (Predicting student performance using data from an Auto-grading system)
Student Performance and Marmoset (Predicting student performance using data from an Auto-grading system)
Features (Predicting student performance using data from an Auto-grading system)
Experiments and Evaluation (Predicting student performance using data from an Auto-grading system)
Reference for (Predicting student performance using data from an Auto-grading system)
Modeling Techniques (Predicting student performance using data from an Auto-grading system)