Showing 1 - 2 of 2 Research Library Publications

Detecting Item Parameter Drift in Small Sample Rasch Equating

Posted: | Daniel Jurich, Chunyan Liu

Applied Measurement Education: Volume 36, Issue 4, Pages 326-339

 

This study examines strategies for detecting parameter drift in small-sample equating, crucial for maintaining score comparability in high-stakes exams. Results suggest that methods like mINFIT, mOUTFIT, and Robust-z effectively mitigate drifting anchor items' effects, while caution is advised with the Logit Difference approach. Recommendations are provided for practitioners to manage item parameter drift in small-sample settings.
 

Category:Assessment-Oriented Research, Reliability/Validity, Scoring

Automated Item Generation with Recurrent Neural Networks

Posted: | M. von Davier

Psychometrika 83, 847–857 (2018)

 

Utilizing algorithms to generate items in educational and psychological testing is an active area of research for obvious reasons: Test items are predominantly written by humans, in most cases by content experts who represent a limited and potentially costly resource. Using algorithms instead has the appeal to provide an unlimited resource for this crucial part of assessment development.

Category:Assessment-Oriented Research, Applications of Technology