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

Detecting High-Functioning Autism In Adults Using Eye Tracking And Machine Learning

Posted: | V. Yaneva, L. A. Ha, S. Eraslan, Y. Yesilada, R. Mitkov

IEEE Transactions on Neural Systems and Rehabilitation Engineering

 

The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism.

Category:Assessment-Oriented Research, Applications of Technology