Enhancing Knowledge Tracing via Breakpoint-Aware Sequence Augmentation

doi:10.1109/TKDE.2026.3700199

Enhancing Knowledge Tracing via Breakpoint-Aware Sequence Augmentation

2026-06-04

0

PRE

AI

DOI：10.1109/TKDE.2026.3700199

Original

Original request for help

Save

Abstract

En 中文

Knowledge Tracing (KT) aims to predict learners’ future responses by modeling historical learning sequences. However, existing works often overlook the severe disruptions in temporal and interaction continuity arising from large time intervals between interactions, which leads to significant prediction errors. Therefore, we identify and validate this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">breakpoint</i> effect through empirical studies, and try to address it via data augmentation (DA). Existing DA methods usually emphasize sequence diversity or similarity while neglecting the breakpoint effect in KT. Hence, we strive to enhance knowledge tracing via breakpoint-aware sequence augmentation and propose Collaborative Learning Path Augmentation (CLPA), a systematic three-phase method to enhance models’ representational capacity and alleviate the breakpoint effects. First, identifying boundary interaction pairs that exhibit the temporal gap and response inconsistency. Second, leveraging cross-sequence collaborative information to infer functional pseudo-paths between identified boundaries. Third, reconstructing and inserting pseudo-learning paths into target sequences to restore sequence continuity; moreover, two refinements of the sampling quantity constraint and the distance-based sampling strategy are proposed to ensure augmentation quality. Comprehensive experiments on four real-world datasets validate the effectiveness of CLPA, which significantly enhances the target KT model’s representational capacity and alleviates the breakpoint effects.

Keywords:

Knowledge tracing

breakpoint effects

data augmentation

collaborative learning path

Enhancing Knowledge Tracing via Breakpoint-Aware Sequence Augmentation

Abstract

Journal

Organization