article · International Journal of Computer Applications
Higher education institutions benefit from identifying student academic challenges early to reduce dropout rates. An application was developed to predict university students' final cumulative grade point average upon graduation, specifically at semester eight. The system evaluates core and non-core course marks earned across semesters one through six to forecast final academic outcomes. Three computational approaches were assessed: Neural Networks, Support Vector Regression, and Linear Regression. While all three techniques produced viable predictions, Support Vector Regression and Linear Regression demonstrated superior performance compared to the Neural Network approach. Consequently, these top-performing models were implemented to form a Student Performance Prediction System. This software tool allows university administrators to conduct predictive assessments well before graduation, offering a practical data-driven mechanism to identify students who may require academic intervention.
Student attrition poses significant challenges for universities. Using existing early course marks to reliably forecast final grades enables academic institutions to detect struggling learners ahead of time. This proactive approach allows administrators and educators to implement timely support and interventions, ultimately helping more students successfully complete their degree programmes.
The models support the creation of a Student Performance Prediction System for higher education institutions seeking to reduce student attrition. The application relies on standard semester course grades to forecast outcomes. Having been tested on student data and translated into a functional prediction application, the technology represents an applied, working prototype that institutions or educational software providers could integrate into existing university management platforms.
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Predicting students" academic performance is very crucial especially for higher educational institutions. This paper designed an application to assist higher education institutions to predict their students" academic performance at an early stage before graduation and decrease students" dropout. The performance of the students was measured based on cumulative grade point average (CGPA) at semester eight. The students" course scores for core and non-core courses from the first semester to the sixth semester are used as predictor variables for predicting the final CGPA8 upon graduation using Neural Network (NN), Support Vector Regression(SVR), and Linear Regression (LR). The study has verified that data mining techniques can be used in predicting students" academic performance in higher educational institutions. All the experiments gave valid results and can be used to predict graduation CGPA. However, comparisons of the experiments were done to determine which approaches perform better than others. Generally, SVR and LR methods performed better than NN. Therefore, we recommend the adoption of SVR and LR methods to predict final CGPA8, and the models can also be used to implement Student Performance Prediction System(SPPS) in a university. Thus, the study has used the models from SVR and LR methods for designing an application to do the prediction task.
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DOI: 10.5120/ijca2018917057
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