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A new Automatic Number Series Item Generator (ANSIG) shows promise for creating tests. Cognitive operators significantly predict item difficulty, and generated items correlate well with ability tests.

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Area of Science:

  • Psychometrics
  • Cognitive Psychology
  • Educational Measurement

Background:

  • Developing automated tools for generating psychometric tests is crucial for efficient assessment.
  • Understanding the cognitive underpinnings of number series tasks is essential for test design.

Purpose of the Study:

  • To evaluate the item properties of a novel Automatic Number Series Item Generator (ANSIG).
  • To assess the construct validity and psychometric predictability of items generated by the ANSIG.

Main Methods:

  • Utilized the numGen R package to develop 11 item models for the ANSIG.
  • Employed Rasch Model and Linear Logistic Test Models (LLTM) for item parameter estimation.
  • Administered a 16-item International Cognitive Ability Resource (ICAR) short form to assess construct validity.

Main Results:

  • A single factor explained performance on tests using ANSIG-generated items.
  • All hypothesized cognitive operators were significant predictors of item difficulty under the LLTM.
  • Moderate to high correlations were found between ANSIG items and ICAR scores, particularly Letter-Numeric-Series items.

Conclusions:

  • The ANSIG demonstrates potential for generating number series items with predictable psychometric properties.
  • Cognitive operators are key predictors of item difficulty in automated test generation.
  • Further research is needed to refine the automatic generation of item pools with desired psychometric characteristics.