Reinforcement learning-based adaptive learning: Rewards improvement considering learning duration

Reinforcement learning (RL) powers the engine of adaptive learning systems which recommend customized learning materials to individual learners in their varying learning states to optimize learning effectiveness. However, some argue that only improving learning effectiveness may be insufficient, particularly if it overly extends learning efforts and requires additional time to work on the recommended materials. Learners with different amounts of prior knowledge consume different amounts of time on the same material. Therefore, designers should consider both the usefulness of the material and the time dedicated to making sense of the materials by individual learners with a specific amount of prior knowledge. To fill this gap, this study proposes a RL-based adaptive learning system wherein reward is improved by considering both factors. We then conducted Monte Carlo simulation studies to verify the effects of the improved reward and uncover the mechanisms for RL recommendation strategies. Results show that the improved reward reduces learners' learning duration substantially due to interpretable recommendation strategies, which results in growing learning efficiency for learners with varying prior knowledge.