New publication: "Where's the Reward? A Review of Reinforcement Learning for Instructional Sequencing"
To help answer these questions, we review the variety of attempts to use RL for instructional sequencing. First, we present a historical narrative of this research area. We identify three waves of research, which gives us a sense of the various communities of researchers that have been interested in this problem and where the field is going. Second, we review all of the empirical research that has compared RL-induced instructional policies to baseline methods of sequencing. We find that over half of the studies found that RL-induced policies significantly outperform baselines. Moreover, we identify five clusters of studies with different characteristics and varying levels of success in using RL to help students learn. We find that reinforcement learning has been most successful in cases where it has been constrained with ideas and theories from cognitive psychology and the learning sciences. However, given that our theories and models are limited, we also find that it has been useful to complement this approach with running more robust offline analyses that do not rely heavily on the assumptions of one particular model. Given that many researchers are turning to deep reinforcement learning and big data to tackle instructional sequencing, we believe keeping these best practices in mind can help guide the way to the reward in using RL for instructional sequencing.
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