Concept

Item Response Theory (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)

Since 1950s IRT has been used in order to test educational environment. Here we are output P(a) (the probability that the user will answer the item correctly) given :

  1. j - the item
  2. θ\theta - student ability level
  3. βj\beta_j - item difficulty level

This can be formulated as (when sigmoid is used as an item response function):

P(α)=σ(θβ)=11+exp(θβj)P(\alpha) = \sigma (\theta - \beta) = \frac {1} {1 + exp(-\theta -\beta_j)}

IRTs can also be used to predict θ\theta or βj\beta_j.

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Updated 2020-11-16

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Data Science