Predicting Student Performance: A Machine Learning Approach to Forecasting Pass/Fail Outcomes | ||||
| منارة الاسكندرية للعلوم التجارية | ||||
| Volume 1, Issue 1, April 2025, Page 188-178 PDF (1.02 MB) | ||||
| Document Type: الدراسات والبحوث العلمية | ||||
| DOI: 10.21608/mauta.2025.428232 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Nawal Mohamed Bahy Eldin; Ayda Elsemsar; Ramy Kamal Amin | ||||
| Teaching Assistant, Management Information Systems& Basic Science Department Egyptian Institute of Alexandria Academy for Management & Accounting | ||||
| Abstract | ||||
| This study aims to predict student performance using machine learning models based on the Open University Learning Analytics Dataset (OULAD). The dataset includes various features such as student assessments, virtual learning environment (VLE) interactions, and demographic data. Several machine learning models were applied, including Logistic Regression, Linear Discriminant Analysis, Random Forest, and Neural Networks, to predict student outcomes (Pass, Fail, or Distinction). The results demonstrate that models incorporating both weighted grades and pass rates outperformed those relying on single features. Although Neural Network models achieved the highest accuracy, they faced challenges in predicting failure cases. This paper offers insights into the performance of different models and proposes directions for future improvements. | ||||
| Keywords | ||||
| Student Performance Prediction; Machine Learning; Neural Networks; Educational Data Mining; Pass/Fail Classification | ||||
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