Showing 1 - 2 of 2 Research Library Publications

Leveraging Machine Learning Technology to Improve Accuracy and Efficiency of Identification of Enemy Item Pairs

Posted: | Ian Micir, Kimberly Swygert, Jean D'Angelo

Journal of Applied Technology: Volume 23 - Special Issue 1 - Pages 30-40

 

The interpretations of test scores in secure, high-stakes environments are dependent on several assumptions, one of which is that examinee responses to items are independent and no enemy items are included on the same forms. This paper documents the development and implementation of a C#-based application that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to produce prioritized predictions of item enemy statuses within a large item bank.

Category:Assessment-Oriented Research, Scoring, Applications of Technology

The Choice of Response Probability in Bookmark Standard Setting: An Experimental Study

Posted: | P. Baldwin, M.J. Margolis, B.E. Clauser, J. Mee, M. Winward

Educational Measurement: Issues and Practice, 39: 37-44

 

This article presents the results of an experiment in which content experts were randomly assigned to one of two response probability conditions: .67 and .80. If the standard-setting judgments collected with the bookmark procedure are internally consistent, both conditions should produce highly similar cut scores.

Category:Assessment-Oriented Research, General Measurement