Usefulness of the Average-of-Sum-of-Outcomes Reward Function (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
The likelihood-based reward function depends on the student's modeled state, so it cannot be applied unless the student is modeled first. The average-of-sum-of-outcomes reward function avoids this dependency because it does not require the student state, making it usable without prior student modeling. Its drawback is that it becomes computationally expensive as the number of exercises grows; sampling exercises into categories can help mitigate this cost.
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Summary of findings (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Limitations (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Sustainability and Ethics (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Future Work (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Usefulness of LSTM for Reward Prediction (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Usefulness of the Average-of-Sum-of-Outcomes Reward Function (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Comparison between likelihood and average of sum of outcomes based reward functions (research objective) (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Usefulness of the Average-of-Sum-of-Outcomes Reward Function (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)