Dataset (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
In this research synthetic dataset was used. The simulators: Half-Life Regression (HLR), GPL (General-Power Law) , Exponential Forgetting Curve (EFC) were used to generate the data. The data had following representation: {q, a, t, d}:
- q - most recent item shown to the student
- a - [0, 1] incorrect/correct answer
- t - timestamp
- d - delay between most recent item and before (Set to 5s)
For LSTM the data was generated by EFC. The data was generated in 3 different ways:
- Random Sample: exercises and answers by students were randomly collected
- Random Policy Tutor: the exercises were chosen by the policy at random while student answer weren't
- Supermemo Tutor: Supermemo algorithm was used
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Dataset (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Systems and Tools (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Experiments (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)