MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing

2026-05-09

AI Summary

Existing knowledge tracing methods struggle to effectively uncover and generalize learners’ deep behavioral patterns, thereby limiting the utilization of collaborative information. This work proposes a universal modeling framework based on meta-behavior sequences: it first constructs meta-behavior sequences to capture high-order behavioral patterns of learners, then designs a parameter-free module to extract global collaborative representations, and finally integrates these representations into various knowledge tracing models through a general-purpose injection mechanism. Without introducing additional parameters, the proposed approach efficiently models collaborative information and significantly enhances the prediction performance of mainstream knowledge tracing models across multiple real-world datasets, demonstrating its effectiveness and broad applicability.

Abstract

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.

Problem

• knowledge tracing
• collaborative information
• behavioral patterns
• generalization
• interaction sequences

Innovation

• meta-behavioral pattern
• knowledge tracing
• collaborative information
• global representation
• sequence transformation