MBP-KT: 从元行为模式中学习全局协作信息以提升知识追踪
MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing
AI总结：
本文提出MBP-KT框架，通过构建元行为序列提取全局协作信息，提升知识追踪模型的性能。
摘要：
基于协作信息的知识追踪（KT）已成为增强学习者知识状态建模的有前景方法。核心思想是从其他学习者的交互序列中提取协作信息以辅助目标学习者的预测。尽管有效，现有方法基于原始交互序列和定制模块，限制了深入捕捉学习行为模式和泛化能力。为此，本文提出一种通用的元行为模式感知框架（MBP-KT）用于KT。具体而言，MBP-KT引入了新的元行为序列构造，将原始交互序列转换为不同元行为模式的组合。这样可以有效保留学习者的行为模式。然后，MBP-KT开发了一个无参数模块，从构造的元行为序列中提取全局协作表示。此外，MBP-KT提供了通用的注入策略，将提取的全局协作信息引入各种下游KT模型，确保协作信息的通用性。在现实世界数据集上的广泛实验结果表明，MBP-KT能一致地提升各种KT模型的性能。
英文摘要：
The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core idea is to extract the collaborative information from interaction sequences of other learners to assist the prediction on the target one. Despite effectiveness, existing methods are built on the raw interaction sequences with tailored modules, which inevitably limits their capacity in deeply capturing learning behavioral patterns and generalization. To this end, we propose a general meta-behavioral pattern-aware framework (MBP-KT) for KT. Specifically, MBP-KT introduces a novel meta-behavioral sequence construction to transform the raw interaction sequences into the combinations of different meta-behavioral patterns. In this way, the learning behavioral patterns of learners can be effectively preserved. Then, MBP-KT develops a parameter-free module to extract the global collaborative representations from the constructed meta-behavioral sequences. Moreover, MBP-KT provides general injection strategies to introduce the extracted global collaborative information into various downstream KT models, ensuring the universality of the collaborative information. Extensive results on real-world datasets demonstrate that MBP-KT can consistently boosts the performance of a wide range of KT models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
