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Knowledge Structure based Simulators

Env Protocol

  • What are provided as environment parameters

    • action space
    • knowledge structure
    • learning item base

  • What is recommended, knowledge or item?

    • knowledge
    • item

Learner

  • Which type is the learner, infinity or finite?
    • infinity
    • finite
  • Which mode is the response of the learner, real or trait?
    • real
    • trait

Item

  • What are the types of items in this environments?

    • learning item
    • test item

  • Is the learning item base same with the test item base?

    • Yes
    • No

  • The difference between learning item base and test item base?

    • completely
    • property
      • knowledge
      • content
      • attribute: learning item do not have difficulty

  • What are included in an item?

    • content
    • knowledge
      • single
      • multiple
    • attribute
      • difficulty

  • What is the relation between item and knowledge in learning item?

    • one-to-one
    • one-to-many
    • many-to-one
    • many-to-many

  • What is the relation between item and knowledge in test item?

    • one-to-one
    • one-to-many
    • many-to-one
    • many-to-many

Reward

  • Step reward: 0
  • Episode reward: $G=\frac{S(T) - S(0)}{S*(T) - S(0)}$

Eq.(1) in [1]

Agent Protocol

  • What is recommended, knowledge or item?
    • knowledge
    • item

Original Code

This is the original implementation in [1]

Reference

[1] Liu Q, Tong S, Liu C, et al. Exploiting cognitive structure for adaptive learning[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 627-635.