Predicting At-Risk University Students Using Reduced Training Vector-Based SVM (RTV-SVM)
Practical Solutions and Value:
Efficiently predicts at-risk and marginal university students, reducing faculty workload and financial strain on institutions.
Reduces training vectors by 59.7% while maintaining high accuracy, achieving 92.2-93.8% accuracy in identifying at-risk students.
Leverages support vector machine (SVM) techniques to enhance prediction in the education sector.
Challenges and Approaches in Learning Analytics for At-Risk Students
Practical Solutions and Value:
Utilizes predictive models like random forest, SVM, and decision trees to forecast student failure and dropout risks.
Addresses challenges in learning analytics such as handling big data and ensuring privacy and security.
RTV-SVM Methodology for Optimized SVM Classification
Practical Solutions and Value:
Consists of four steps to enhance classification efficiency by minimizing the number of training vectors while preserving accuracy.
Applies tier-1 and tier-2 eliminations to significantly reduce training vectors without sacrificing accuracy.
Performance Comparison Between RTV-SVM and Related Methods
Practical Solutions and Value:
Demonstrates superior performance in predicting at-risk students, achieving higher accuracy than other methods.
Outperforms models designed for more complex predictions, making it a strong tool for predicting student outcomes.
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