Experimental Setup (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
The experimental setup involved the following parameters and configurations:
- 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, and Threshold
- EFC: was sampled from a log-normal distribution where .
- HLR: , where represents (number of attempts, number of correct, number of incorrect, one-hot encoding of item out of items).
- GPL: Student ability . Sampled item difficulties d sim mathcal{N}(1, 1) and . The delay coefficient is , window coefficients are , and the number of windows is 5.
- TRPO and TNPG: Settings were kept identical.
- LSTM: Configured with 20 units, a dense unit with sigmoid activation, the Adam optimizer, and 2 hidden states.
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Experimental Setup (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)
Reward functions and performance metrics (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)