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New Item Selection Method Accommodating Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing:

Junjie Li1, Lihua Ma1, Pingfei Zeng1

  • 1Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China.

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Summary

A new method, maximum limitation global discrimination index (MLGDI), balances attribute coverage and item exposure in cognitive diagnostic tests. MLGDI demonstrated superior attribute classification accuracy compared to other methods under practical constraints.

Keywords:
attribute discrimination indexbalance attribute coveragecognitive diagnostic computerized adaptive testingitem exposure controlmastery pattern correct classification rate

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Computerized Adaptive Testing

Background:

  • Cognitive diagnostic computerized adaptive testing (CD-CAT) requires effective item selection methods.
  • Balancing attribute coverage and controlling item exposure are critical challenges in CD-CAT.

Purpose of the Study:

  • To introduce and evaluate the Maximum Limitation Global Discrimination Index (MLGDI) for CD-CAT.
  • To compare MLGDI's performance against existing methods regarding attribute coverage balance and item exposure control.

Main Methods:

  • Development of MLGDI based on the Maximum Deviation Global Discrimination Index (MDGDI).
  • Simulation study comparing MLGDI with GDI, MMGDI, SWDGDI, and CP_SWDGDI.
  • Evaluation of attribute classification accuracy, attribute coverage, and item exposure rates.

Main Results:

  • MDGDI achieved the highest attribute classification accuracy when only attribute coverage balance was considered.
  • MLGDI demonstrated the highest attribute classification accuracy when both attribute coverage balance and item exposure control were implemented.
  • Incorporating item exposure control slightly reduced attribute classification accuracy compared to methods without it.
  • MLGDI outperformed other methods in achieving attribute-coverage requirements, controlling item exposure, and maintaining attribute correct classification rates.

Conclusions:

  • MLGDI is an effective method for CD-CAT, balancing attribute coverage and item exposure.
  • The findings provide insights into optimizing item selection strategies in adaptive testing environments.
  • MLGDI offers a practical solution for enhancing the psychometric properties of CD-CAT.