Adaptive Knowledge Transfer for Cross-Disciplinary Cold-Start Knowledge Tracing

TL;DR

This paper introduces a novel framework for cross-disciplinary cold-start knowledge tracing that leverages mixture-of-experts and adversarial networks to improve student knowledge state modeling when data is scarce in the target discipline.

Contribution

It proposes a new method combining clustering, mixture-of-experts, and adversarial learning to better transfer knowledge across disciplines with limited data.

Findings

Effective in 20 extreme cross-disciplinary cold-start scenarios

Outperforms existing simple mapping methods

Mitigates small-sample limitations through adversarial feature separation

Abstract

Cross-Disciplinary Cold-start Knowledge Tracing (CDCKT) faces a critical challenge: insufficient student interaction data in the target discipline prevents effective knowledge state modeling and performance prediction. Existing cross-disciplinary methods rely on overlapping entities between disciplines for knowledge transfer through simple mapping functions, but suffer from two key limitations: (1) overlapping entities are scarce in real-world scenarios, and (2) simple mappings inadequately capture cross-disciplinary knowledge complexity. To overcome these challenges, we propose Mixed of Experts and Adversarial Generative Network-based Cross-disciplinary Cold-start Knowledge Tracing Framework. Our approach consists of three key components: First, we pre-train a source discipline model and cluster student knowledge states into K categories. Second, these cluster attributes guide a mixture-of-experts network through a gating mechanism, serving as a cross-domain mapping bridge. Third, an adversarial discriminator enforces feature separation by pulling same-attribute student features closer while pushing different-attribute features apart, effectively mitigating small-sample limitations. We validate our method's effectiveness across 20 extreme cross-disciplinary cold-start scenarios.