Statistical Evaluation Procedures (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
The study used four statistical evaluation procedures. (1) When varying the number of items, it formed distributions of mean pairwise reward differences across student models, assessed normality with the Shapiro-Wilk test, and, because variance homogeneity did not hold, used Welch's two-sample test followed by the Games-Howell test. (2) It compared TRPO and TNPG using rewards from all episodes and runs and the Kruskal-Wallis test. (3) It compared likelihood-based and average-of-sum-of-outcomes reward functions with the Kruskal-Wallis test, followed by Dunn's post hoc test with Bonferroni correction. (4) It evaluated TRPO with LSTM reward shaping across tutors using the Kruskal-Wallis test, followed by Dunn's test with Bonferroni correction, and also compared the LSTM-based approach with other methods used by the DRL agents.
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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)
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)
Reward functions and performance metrics (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Experimental design for evaluating TRPO performance with reward shaping (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Statistical Evaluation Procedures (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)