The expansion of large-scale online education platforms has made vast amounts of student interaction data available for knowledge tracing (KT). KT models estimate students' concept mastery from interaction data, but their performance is sensitive to input data quality. Gaming behaviors, such as excessive hint use, may misrepresent students' knowledge and undermine model reliability. However, systematic investigations of how different types of gaming behaviors affect KT remain scarce, and existing studies rely on costly manual analysis that does not capture behavioral diversity. In this study, we conceptualize gaming behaviors as a form of data poisoning, defined as the deliberate submission of incorrect or misleading interaction data to corrupt a model's learning process. We design Data Poisoning Attacks (DPAs) to simulate diverse gaming patterns and systematically evaluate their impact on KT model performance. Moreover, drawing on advances in DPA detection, we explore unsupervised approaches to enhance the generalizability of gaming behavior detection. We find that KT models' performance tends to decrease especially in response to random guess behaviors. Our findings provide insights into the vulnerabilities of KT models and highlight the potential of adversarial methods for improving the robustness of learning analytics systems.

翻译：随着大规模在线教育平台的扩展，海量学生交互数据为知识追踪（KT）提供了研究基础。KT模型通过交互数据评估学生对知识概念的掌握程度，但其性能对输入数据质量高度敏感。诸如过度使用提示等游戏化行为可能扭曲学生的真实知识水平，从而削弱模型的可靠性。然而，关于不同类型游戏化行为如何影响KT的系统性研究仍较为匮乏，现有研究多依赖成本高昂的人工分析，难以捕捉行为多样性。本研究将游戏化行为概念化为一种数据投毒形式，即通过故意提交错误或误导性交互数据以破坏模型学习过程。我们设计了数据投毒攻击（DPAs）来模拟多样化的游戏行为模式，并系统评估其对KT模型性能的影响。此外，借鉴DPA检测领域的最新进展，我们探索了无监督方法来提升游戏化行为检测的泛化能力。研究发现，KT模型性能的下降尤其与随机猜测行为密切相关。本研究揭示了KT模型的脆弱性，并强调了对抗性方法在提升学习分析系统鲁棒性方面的潜力。