Abstract
We introduce KUL-KT, a biologically inspired architecture for knowledge tracing (KT), combining Hebbian memory encoding with gradient-based consolidation in a scalable, input-agnostic framework. KUL-KT adapts the principle of memory consolidation in neural systems, to student modeling by introducing two key innovations: (i) a time-decaying Hebbian memory update that enables graceful forgetting, and (ii) a novel Loss-aligned Internal Target (LIT) method to compute an ideal internal state, allowing continual learning without backpropagation through time. The architecture consists of a fast Hebbian memory that captures each learner interaction via a single associative update, and a slower linear network that consolidates recalled samples through gradient descent. This design enables few-shot personalization and natural forgetting without storing raw data or relying on large cohort training. Operating entirely in embedding space, KUL-KT supports both structured (tabular) and unstructured (short-answer) inputs. Empirically, KUL-KT outperforms strong baselines on ten public KT benchmarks in rank-sensitive metrics such as nDCG and Recall@10. In a classroom deployment, KUL-KT personalized quizzes from short-answer data, leading to improved learner-perceived helpfulness and reduced difficulty (p < 0.05). Ablation studies confirm that Hebbian decay and LIT are critical for continual adaptation. Compared to a strong graph-based KT model, KUL-KT trains 1.75x faster and uses 99.01\% less memory. These results position KUL-KT as a biologically grounded, memory-efficient, and input-flexible framework for personalized learning at scale.
Abstract (translated)
我们介绍了KUL-KT，这是一种生物启发的知识追踪（KT）架构，它结合了海布记忆编码与基于梯度的巩固机制，并在一个可扩展且不受输入限制的框架中实现了这一点。KUL-KT通过引入两个关键创新，将神经系统的记忆巩固原理应用到学生建模上：(i) 一种随时间衰减的海布记忆更新方式，使优雅遗忘成为可能；(ii) 一种新颖的目标与损失对齐内部目标（LIT）方法来计算理想内部状态，从而允许不通过时间反向传播进行持续学习。该架构包括一个快速的海布记忆，每次捕捉到每个学习者互动时都会通过单一联想更新来进行记录，以及一个较慢的线性网络，它通过梯度下降巩固被召回的数据样本。这种设计使得少量个性化和自然遗忘成为可能，同时不需要存储原始数据或依赖大规模群体训练。整个操作完全在嵌入空间中进行，KUL-KT支持结构化（表格）和非结构化（简答题）输入。 实验上，在十个公开的知识追踪基准测试中，KUL-KT在nDCG和Recall@10等敏感排名的度量标准上超过了强大的基线模型。在一个教室部署案例中，KUL-KT利用简答题数据为每位学生个性化测验，提高了学习者感知到的帮助效果，并降低了难度（p < 0.05）。删减研究证实了海布衰减和LIT对于持续适应性至关重要。与一个强大的基于图的KT模型相比，KUL-KT训练速度快1.75倍且内存使用量减少了99.01%。 这些结果表明，KUL-KT作为一种生物基础、记忆效率高并且输入灵活的知识追踪框架，在大规模个性化学习中具有重要位置。
URL
https://arxiv.org/abs/2507.00032
