University of Twente Student Theses
Enhancing Student Engagement in VLEs for Academic Success : Machine Learning Approach
Sberlo, Yarden (2024) Enhancing Student Engagement in VLEs for Academic Success : Machine Learning Approach.
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| Abstract: | With the increasing prevalence of Virtual Learning Environments (VLEs) in education, understanding how student engagement impacts academic success has become essential. This study investigates the correlation between various engagement metrics and dropout rates in courses given through VLEs, utilizing the KDD Cup 2015 dataset from XuetangX University. Logistic regression (LR) analysis identified key engagement features correlating with academic outcomes, which were then used to train a Gradient Boosting Machine (GBM). The GBM model demonstrated high performance with an accuracy of 88%, surpassing that of LR. To further explore practical applications, a web app prototype was developed, integrating the predictive model and visualizations of student engagement data. This prototype was evaluated by a sample of students and teachers from the University of Twente. Survey results indicated that both students and teachers found the application easy to use and beneficial for enhancing engagement and academic performance. Notably, the prediction model and visual analytics features were highly valued by participants. This research underlines the potential of using machine learning and visualized analytics to improve educational outcomes in VLEs, providing valuable insights for educators and learners alike. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/101175 |
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