Supermemo System
This is the algorithm for the supermemo (SP2) which was described in the paper:
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Split knowledge
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E-Factor = 2.5, for all items
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Repeat items using the following intervals: I(1):=1 I(2):=6 for n>2: I(n):=I(n-1)*EF where: I(n) - inter-repetition interval after the n-th repetition (in days), EF - E-Factor (easiness of memorizing and retaining a given item in memory)
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There is grade scale 0-5: 5 - PERFECT 4 - Correct + hesitation 3 - Correct + took more time 2 - Incorrect + correct easy to recall 1 - Incorrect + correct remembered 0 - Blackout
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After this we change E-Factors using the formula: EF_new:=EF+(0.1-(5-q)*(0.08+(5-q)*0.02)) q - 0-5 range; EF_new - new E-Factor, if EF < 1.3 EF = 1.3
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If q < 3 then repeat 1-5 without changing EF.
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After on a specific day the session is finished, repeat the procedure until at least q=4 is achieved for all the items.
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Tags
Data Science
Related
Background (Accelerating Human Learning With Deep Reinforcement Learning)
Spaced Repetition
Leitner System
Supermemo System
Reinforcement Learning
Intelligent Tutoring Systems (Using deep reinforcement learning for personalizing review sessions on e-learning platforms with spaced repetition)
Relation between Tutoring Systems and Student learning
Trust Region Policy Optimization
Truncated Natural Policy Gradient
Recurrent Neural Network (RNN)