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Adetuberu, Oluwadamilare (2023) Predictive Analytics for Enhancing Student Success in the UK: A Machine Learning Approach. Masters thesis, Dublin, National College of Ireland.
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Abstract
Student success is an essential part of human capital development in society. This study is motivated by the desire to effectively predict students’ success using Machine Learning (ML) algorithms based on educational data, thereby contributing to the enhancement of student success overall.
A case study approach of an on-line learning environment in the UK is adopted. By analysing the dataset from the Open University Learning Analytics (OULAD), the most effective ML model in forecasting outcomes, based on student’s academic record, demographic information and student behaviours records were investigated. Through an experimental approach, techniques including Logistic regression, Decision trees, RandomForest, and Gradient Boosting Machine were employed and evaluated using metrics such as Accuracy score, Precision, Recall, F1-score and ROC AUC, Log loss and a five-fold cross validation. The model ROC AUC was 0.790, 0.831, 0.798, 0.808, respectively. This research contributes to the field of Predictive Analytics, Learning Analytics and Educational Data Mining
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Shani, Anu UNSPECIFIED |
| Uncontrolled Keywords: | Educational Data Mining (EDM); Predictive Analytics; Student Success; Machine Learning; Learning Analytics (LA) |
| Subjects: | L Education > L Education (General) Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science L Education > LC Special aspects / Types of education > E-Learning Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 29 Apr 2025 16:37 |
| Last Modified: | 06 May 2025 13:42 |
| URI: | https://norma.ncirl.ie/id/eprint/7483 |
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