Experimental Setup (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
- Number of items : 30
- Number of runs: 10
- Number of episodes per run: 100
- Number of steps per episode: 200
- Delay between steps: 5s
- Four baseline policies: Leitner, SuperMemo, Random, Threshold
- EFC sampled from a log-normal distribution $\log\theta \sim N \log (0.077, 1)
- HLR = (num attempts, num correct, num incorrect, one-hot encoding of item i out of n items).
- GPL student ability sample item difficulties and delay coefficient l, window coefficients and number of windows 5.
- TRPO and TNPG settings were the same
- LSTM: 20 units, dense unit with sigmoid, Adam optimizer. 2 hidden states.
- DRL
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Experimental Setup (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Reward functions and performance metrics (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Training the LSTM (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Relation between rewards and thresholds (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of RL agent when the number of items are varied (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of TRPO vs. TNPG algorithms (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Performance of TRPO with reward shaping (research objective) (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)
Evaluation (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)