A Comparative Analysis for GPA Prediction of Undergraduate Students Using Machine and Deep Learning

We utilized extensive and diverse course records, encompassing several academic years and programs, to conduct a comparative analysis of various Machine and Deep Learning methodologies, assessing their efficacy through performance metrics. The developed ML/DL algorithms use Grade Point Averages (GPAs) of courses and semesters as explanatory features to predict the student’s final GPA, which is the target value of the models. Based on the results, the linear and bagging regression models have the best Mean Absolute Error (MAE) performance metric. Data on early courses and semesters are used to ensure enough time for academic intervention.

Recommended citation: alnomay, Ibrahim; Alfadhly, Abdullah; Alqarni, Aali .(2024). A Comparative Analysis for GPA Prediction of Undergraduate Students Using Machine and Deep Learning. International Journal of Information and Education Technology. [http://academicpages.github.io/files/paper3.pdf](https://www.ijiet.org/show-200-2670-1.html)