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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.18925 |
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| _version_ | 1866908504318541824 |
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| author | Xiao, Qian Breathnach, Conn Ghergulescu, Ioana O'Sullivan, Conor Johnston, Keith Wade, Vincent |
| author_facet | Xiao, Qian Breathnach, Conn Ghergulescu, Ioana O'Sullivan, Conor Johnston, Keith Wade, Vincent |
| contents | The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum-based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for tracking progress, identifying struggling students, and alleviating disparities among students. Such profiling requires measuring student behaviors and performance across different aspects, such as content coverage, learning intensity, and proficiency in different concepts within a learning topic.
In this study, we introduce CTGraph, a graph-level representation learning approach to profile learner behaviors and performance in a self-supervised manner. Our experiments demonstrate that CTGraph can provide a holistic view of student learning journeys, accounting for different aspects of student behaviors and performance, as well as variations in their learning paths as aligned to the curriculum structure. We also show that our approach can identify struggling students and provide comparative analysis of diverse groups to pinpoint when and where students are struggling. As such, our approach opens more opportunities to empower educators with rich insights into student learning journeys and paves the way for more targeted interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18925 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems Xiao, Qian Breathnach, Conn Ghergulescu, Ioana O'Sullivan, Conor Johnston, Keith Wade, Vincent Artificial Intelligence The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum-based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for tracking progress, identifying struggling students, and alleviating disparities among students. Such profiling requires measuring student behaviors and performance across different aspects, such as content coverage, learning intensity, and proficiency in different concepts within a learning topic. In this study, we introduce CTGraph, a graph-level representation learning approach to profile learner behaviors and performance in a self-supervised manner. Our experiments demonstrate that CTGraph can provide a holistic view of student learning journeys, accounting for different aspects of student behaviors and performance, as well as variations in their learning paths as aligned to the curriculum structure. We also show that our approach can identify struggling students and provide comparative analysis of diverse groups to pinpoint when and where students are struggling. As such, our approach opens more opportunities to empower educators with rich insights into student learning journeys and paves the way for more targeted interventions. |
| title | Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.18925 |