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Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models
Bao Sheng Loe1, Luning Sun2, Filip Simonfy3
1The Psychometrics Centre, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge CB2 1AG, UK. bsl28@cam.ac.uk.
Journal of Intelligence
|June 5, 2019
Summary
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.
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.
Keywords:
Linear Logistic Test ModelsRasch modelautomatic item generationcognitive modelsnumber seriesMore Related Videos
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