Azizi Hadis, Amini Mohammad Sadra, Sulaimany Sadegh, Mafakheri Aso
Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
Sci Rep. 2025 Sep 1;15(1):32036. doi: 10.1038/s41598-025-17760-1.
This paper introduces visibility graph analysis as a supplementary approach for examining educational time series data, particularly in online learning environments. By converting temporal data into graph representations, we uncover previously hidden patterns and relationships in student interactions, enabling more effective analysis, classification, and prediction of learning outcomes. Through a rigorous case study using the Open University Learning Analytics Dataset, we demonstrate how visibility graph metrics can accurately predict at-risk online students based on their clickstream patterns, achieving classification accuracy exceeding 87% using gradient boosting algorithms. Our novel methodology outperforms several recent deep learning approaches while providing interpretable insights about student behavior through graph-theoretical features such as global efficiency, assortativity coefficient, and betweenness centrality. This research establishes visibility graph analysis as an innovative tool in educational data mining that complements traditional machine learning techniques, opening new avenues for early intervention strategies and personalized learning pathways. However, accurately modeling the problem and selecting the appropriate type of visibility graph for the educational time series data remains dependent on the researcher's knowledge.
本文介绍了可视性图分析，作为一种用于检查教育时间序列数据的补充方法，特别是在在线学习环境中。通过将时间数据转换为图形表示，我们揭示了学生互动中以前隐藏的模式和关系，从而能够对学习成果进行更有效的分析、分类和预测。通过使用开放大学学习分析数据集进行的严格案例研究，我们展示了可视性图指标如何根据学生的点击流模式准确预测有风险的在线学生，使用梯度提升算法实现了超过87%的分类准确率。我们的新方法优于最近的几种深度学习方法，同时通过诸如全局效率、 assortativity系数和中介中心性等图论特征提供有关学生行为的可解释见解。本研究将可视性图分析确立为教育数据挖掘中的一种创新工具，它补充了传统机器学习技术，为早期干预策略和个性化学习路径开辟了新途径。然而，准确地对问题进行建模并为教育时间序列数据选择合适类型的可视性图仍然依赖于研究人员的知识。
