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RESEARCH LIBRARY

Showing 1 - 5 of 5 Reseach Library Publications
Posted: March 1, 2019 | J. Salt, P. Harik, M. A. Barone

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 314-316

 

The United States Medical Licensing Examination Step 2 Clinical Skills (CS) exam uses physician raters to evaluate patient notes written by examinees. In this Invited Commentary, the authors describe the ways in which the Step 2 CS exam could benefit from adopting a computer-assisted scoring approach that combines physician raters’ judgments with computer-generated scores based on natural language processing (NLP).

Posted: October 1, 2018 | Z. Cui, C. Liu, Y. He, H. Chen

Journal of Educational Measurement, 55: 582-594

 

This article proposes and evaluates a new method that implements computerized adaptive testing (CAT) without any restriction on item review. In particular, it evaluates the new method in terms of the accuracy on ability estimates and the robustness against test‐manipulation strategies. This study shows that the newly proposed method is promising in a win‐win situation: examinees have full freedom to review and change answers, and the impacts of test‐manipulation strategies are undermined.

Posted: August 10, 2018 | S. Tackett, M. Raymond, R. Desai, S. A. Haist, A. Morales, S. Gaglani, S. G. Clyman

Medical Teacher, 40:8, 838-841

 

Adaptive learning requires frequent and valid assessments for learners to track progress against their goals. This study determined if multiple-choice questions (MCQs) “crowdsourced” from medical learners could meet the standards of many large-scale testing programs.

Posted: April 3, 2018 | I. Kirsch, W. Thorn, M. von Davier

Quality Assurance in Education, Vol. 26 No. 2, pp. 150-152

 

An introduction to a special issue of Quality Assurance in Education featuring papers based on presentations at a two-day international seminar on managing the quality of data collection in large-scale assessments.

Posted: March 12, 2018 | 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.