Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning
This work addresses the challenge of personalized learning for learners by integrating curiosity into recommendation strategies, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of designing recommendation strategies in adaptive learning systems by incorporating curiosity to guide learners spontaneously, resulting in a personalized learning mode that is both efficient and enjoyable, with numeric analyses demonstrating its power in large continuous knowledge state spaces.
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners. As a critical motivate for human behaviors, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we aim to incorporate the element of curiosity for guiding learners to study spontaneously. In this paper, a curiosity-driven recommendation policy is proposed under the reinforcement learning framework, allowing for a both efficient and enjoyable personalized learning mode. Given intrinsic rewards from a well-designed predictive model, we apply the actor-critic method to approximate the policy directly through neural networks. Numeric analyses with a large continuous knowledge state space and concrete learning scenarios are used to further demonstrate the power of the proposed method.