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Arxiv
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Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
- Researchers introduce KUL-KT, a biologically inspired architecture for knowledge tracing (KT) that combines Hebbian memory encoding with gradient-based consolidation in a scalable framework.
- KUL-KT adapts the principle of memory consolidation in neural systems for student modeling by incorporating a time-decaying Hebbian memory update for graceful forgetting and a Loss-aligned Internal Target (LIT) method for continual learning without backpropagation through time.
- The architecture comprises a fast Hebbian memory capturing learner interactions and a slower linear network consolidating recalled samples through gradient descent, allowing for few-shot personalization and natural forgetting without storing raw data or relying on large cohort training.
- In empirical testing, KUL-KT outperforms strong baselines on ten public KT benchmarks, leading to improved learner-perceived helpfulness and reduced difficulty in a classroom setting. Ablation studies confirm the importance of Hebbian decay and LIT for continual adaptation, positioning KUL-KT as a memory-efficient and input-flexible framework for personalized learning at scale.
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