基于认知自适应扩散模型，提升学习路径推荐的个性化与不确定性感知能力
Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion
- 用高斯LSTM建模认知状态分布，捕捉学习者真实水平
- 目标导向编码器动态对齐概念语义，生成个性化嵌入
- 采用生成式扩散模型预测最优学习路径，更稳定可靠
学习路径推荐（LPR）对个性化教育至关重要，但现有方法常忽略历史交互中的不确定性（如偶然猜中或失误），且难以适配多样学习目标。本文提出U-GLAD（不确定性感知的生成式学习路径推荐，带认知自适应扩散）。为缓解表示偏差，框架将认知状态建模为概率分布，通过高斯LSTM捕捉学习者潜在真实状态；为实现高度个性化，目标导向的概念编码器利用多头注意力和目标特异性变换，动态对齐概念语义与个体学习目标，生成专属嵌入。不同于传统判别式排序方法，本模型采用生成式扩散模型预测下一最优概念的潜在表示。在三个公开数据集上的大量实验表明，U-GLAD显著优于代表性基线。进一步分析证实其在感知交互不确定性及提供稳定、目标驱动的学习路径方面表现更优。
原文摘要 · Abstract (English)
Learning Path Recommendation (LPR) is critical for personalized education, yet current methods often fail to account for historical interaction uncertainty (e.g., lucky guesses or accidental slips) and lack adaptability to diverse learning goals. We propose U-GLAD (Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion). To address representation bias, the framework models cognitive states as probability distributions, capturing the learner's underlying true state via a Gaussian LSTM. To ensure highly personalized recommendation, a goal-oriented concept encoder utilizes multi-head attention and objective-specific transformations to dynamically align concept semantics with individual learning goals, generating uniquely tailored embeddings. Unlike traditional discriminative ranking approaches, our model employs a generative diffusion model to predict the latent representation of the next optimal concept. Extensive evaluations on three public datasets demonstrate that U-GLAD significantly outperforms representative baselines. Further analyses confirm its superior capability in perceiving interaction uncertainty and providing stable, goal-driven recommendation paths.
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