# Exploiting Cognitive Structure for Adaptive Learning

arXiv: https://arxiv.org/abs/1905.12470 · HTML: https://mlsanity.com/paper/1905.12470/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 30 of 37)

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- [Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation](https://mlsanity.com/paper/2405.12442/) (2024)
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- [Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis](https://mlsanity.com/paper/2502.13996/) (2025)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
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- [Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style](https://mlsanity.com/paper/2505.19173/) (2025)
- [Towards a General Pre-training Framework for Adaptive Learning in MOOCs](https://mlsanity.com/paper/2208.04708/) (2022)
- [A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining](https://mlsanity.com/paper/2309.04761/) (2023)
- [Simulating Human-Like Learning Dynamics with LLM-Empowered Agents](https://mlsanity.com/paper/2508.05622/) (2025)
- [A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments](https://mlsanity.com/paper/2410.15054/) (2024)
- [Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call](https://mlsanity.com/paper/2606.15225/) (2026)
- [A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability](https://mlsanity.com/paper/2302.08673/) (2023)
- [Quiz-based Knowledge Tracing](https://mlsanity.com/paper/2304.02413/) (2023)
- [Dual-State Personalized Knowledge Tracing with Emotional Incorporation](https://mlsanity.com/paper/2405.16799/) (2024)
- [Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild](https://mlsanity.com/paper/2606.11744/) (2026)
- [Prerequisite-driven Q-matrix Refinement for Learner Knowledge Assessment: A Case Study in Online Learning Context](https://mlsanity.com/paper/2208.12642/) (2022)
- [Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF](https://mlsanity.com/paper/2211.04759/) (2022)
- [AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing](https://mlsanity.com/paper/2504.04706/) (2025)
- [Personalized Education with Ranking Alignment Recommendation](https://mlsanity.com/paper/2507.23664/) (2025)
- [Cognitive Structure Generation: From Educational Priors to Policy Optimization](https://mlsanity.com/paper/2508.12647/) (2025)
- [DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education](https://mlsanity.com/paper/2510.11076/) (2025)
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## Abstract (arXiv)

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.