Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems

2025-08-26

AI Summary

Intelligent tutoring systems (ITS) in curriculum-based online learning risk exacerbating academic achievement gaps among students. Method: This paper proposes CTGraph, the first self-supervised graph representation learning framework that explicitly incorporates curriculum structure priors to construct student behavior graphs—modeling multidimensional learning signals including learning pathways, content coverage, engagement intensity, and conceptual mastery. A graph neural network performs graph-level encoding to enable cross-cohort behavioral comparison and stage-wise difficulty localization. Contribution/Results: Experiments demonstrate that CTGraph accurately identifies at-risk students, pinpoints optimal intervention timing with fine-grained temporal resolution, and localizes specific knowledge gaps. It significantly enhances personalized instructional support while providing interpretable, pedagogically grounded insights—offering a transparent, equity-oriented technical pathway for adaptive education.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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 repre- sentation 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.

Problem

• Profiling student behaviors to identify struggling learners
• Measuring performance gaps in curriculum-based online systems
• Tracking learning progress across content and proficiency aspects

Innovation

• Graph-level representation learning for profiling
• Self-supervised student behavior modeling approach
• Curriculum-aligned learning path variation analysis