University of Twente Student Theses
Secure AI-Enhanced Student Engagement Analysis
Runhaar, T.P. (2024) Secure AI-Enhanced Student Engagement Analysis.
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| Abstract: | This research project aims to develop a comprehensive understanding of student engagement in online university settings and how AI can be utilised to analyse and enhance it. The study begins with a thorough review of existing studies to identify crucial factors influencing student engagement, focusing on three primary facets: behavioral, emotional, and cognitive engagement. Further an analysis is made on the Open University Learning Analytics dataset to identify patterns that link student engagement with academic achievement. This analysis employs a Light Gradient Boosting Machine model to classify students by results based on their interactions with the Virtual Learning Environment. Finally, in the project, a web application has been developed that leverages these insights. It is designed to provide educators with analytics to understand their students' engagement. It includes features like analysis of student results, submissions, and identification of at-risk students. Security measures such as JWT tokens, encryption and password hashing are implemented to ensure data integrity and confidentiality. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science, 81 education, teaching |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/98189 |
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