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General Diagnostic Model (GDM)

Research Method · All research methods
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Description

The General Diagnostic Model (GDM) is a cognitive-diagnosis student model in which learners carry ability vectors with one component per knowledge component and questions carry discrimination vectors and an easiness parameter, combined through a logistic probability of a correct answer. The article states it "choose[s] to calibrate a General Diagnostic Model, because it predicts student performance better than other cognitive diagnosis models", citing its prior work, and assumes the learner's level does not change during the initial question bundle. Estimated ability components can be interpreted as degrees of mastery per knowledge component.

Accounts

  • General Diagnostic Model for learner ability estimation over knowledge components: The General Diagnostic Model (GDM) is a cognitive-diagnosis student model in which learners carry ability vectors with one component per knowledge component and questions carry discrimination vectors and an easiness parameter, combined through a logistic probability of a correct answer. The article states it "choose[s] to calibrate a General Diagnostic Model, because it predicts student performance better than other cognitive diagnosis models", citing its prior work, and assumes the learner's level does not change during the initial question bundle. Estimated ability components can be interpreted as degrees of mastery per knowledge component. (Jill-Jêenn Vie et al. (2018))

Claims

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Key Sources

  • Jill-Jêenn Vie, Fabrice Popineau, Éric Bruillard, Yolaine Bourda. (2018). Automated Test Assembly for Handling Learner Cold-Start in Large-Scale Assessments. Int J Artif Intell Educ. https://doi.org/10.1007/s40593-017-0163-y
Page metadata
Field Value
type research-method
id general-diagnostic-model-gdm
title General Diagnostic Model (GDM)
description The General Diagnostic Model (GDM) is a cognitive-diagnosis student model in which learners carry ability vectors with one component per knowledge component and questions carry discrimination vectors and an easiness parameter, combined through a logistic probability of a correct answer.
status review
generated by process:settle-candidates on 2026-10-27
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