# Exploiting Cognitive Structure for Adaptive Learning

## TL;DR

This paper introduces CSEAL, a novel framework that integrates cognitive structure modeling with reinforcement learning to improve personalized learning path recommendations.

## Contribution

It combines knowledge level tracking, knowledge structure navigation, and actor-critic algorithms into a unified adaptive learning framework.

## Key findings

- CSEAL outperforms existing methods in recommendation accuracy.
- The framework demonstrates robustness across different datasets.
- It effectively personalizes learning paths based on cognitive states.

## Abstract

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.