1Cademy - Augmenting Knowledge Tracing by Considering Forgetting Behavior (Proposed Approach)

Concept

Augmenting Knowledge Tracing by Considering Forgetting Behavior (Proposed Approach)

Utilize the DKT model as our base model because it is a deep neural network that can easily incorporate multiple input sources and capture the nonlinear dynamics among them
Extends DKT so that it can consider forgetting, the model can adapt the student performance to the student’s forgetting
Consider the following three features: 1) repeated time gap: the lag time between an interaction and the previous interaction with the same skill id, 2) sequence time gap: the lag time between an interaction and the previous interaction in the sequence; the skill id of an interaction does not matter, and 3) past trial counts: the number of times a student answers questions with the same skill id.

Updated 2021-07-17

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