Showing 1 - 2 of 2 Research Library Publications

A Comparison of Strategies for Smoothing Parameter Selection for Mixed-Format Tests Under the Random Groups Design

Posted: | C. Liu, M. J. Kolen

Journal of Educational Measurement: Volume 55, Issue 4, Pages 564-581

 

Smoothing techniques are designed to improve the accuracy of equating functions. The main purpose of this study is to compare seven model selection strategies for choosing the smoothing parameter (C) for polynomial loglinear presmoothing and one procedure for model selection in cubic spline postsmoothing for mixed‐format pseudo tests under the random groups design.

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