Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models
1
The Psychometrics Centre, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge CB2 1AG, UK
2
Department of Psychology, University of East Anglia, Norwich NR4 7TJ, UK
3
Faculty of Statistics, TU Dortmund University, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
J. Intell. 2018, 6(2), 20; https://doi.org/10.3390/jintelligence6020020
Received: 17 January 2018 / Revised: 27 February 2018 / Accepted: 26 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Cognitive Models in Intelligence Research)
This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG). The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1) short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s) (LLTM) were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties.
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Keywords:
cognitive models; automatic item generation; number series; Rasch model; Linear Logistic Test Models
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MDPI and ACS Style
Loe, B.S.; Sun, L.; Simonfy, F.; Doebler, P. Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models. J. Intell. 2018, 6, 20.
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