A new person-fit method based on machine learning in CDM in education

Zhemin Zhu, David Arthur, Hua Hua Chang

Research output: Contribution to journalArticlepeer-review

Abstract

Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.

Original languageEnglish (US)
JournalBritish Journal of Mathematical and Statistical Psychology
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • aberrant response
  • cognitive diagnosis methods
  • machine learning
  • neural network
  • person-fit

ASJC Scopus subject areas

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)

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