Showing 1 - 6 of 6 Research Library Publications
Posted: | Victoria Yaneva, Peter Baldwin, Le An Ha, Christopher Runyon

Advancing Natural Language Processing in Educational Assessment: Pages 167-182

 

This chapter discusses the evolution of natural language processing (NLP) approaches to text representation and how different ways of representing text can be utilized for a relatively understudied task in educational assessment – that of predicting item characteristics from item text.

Posted: | Polina Harik, Janet Mee, Christopher Runyon, Brian E. Clauser

Advancing Natural Language Processing in Educational Assessment: Pages 58-73

 

This chapter describes INCITE, an NLP-based system for scoring free-text responses. It emphasizes the importance of context and the system’s intended use and explains how each component of the system contributed to its accuracy.

Posted: | Y.S. Park, A. Morales, L. Ross, M. Paniagua

Evaluation & the Health Professions: Volume: 43 issue: 3, page(s): 149-158

 

This study examines the innovative and practical application of DCM framework to health professions educational assessments using retrospective large-scale assessment data from the basic and clinical sciences: National Board of Medical Examiners Subject Examinations in pathology (n = 2,006) and medicine (n = 2,351).

Posted: | F.S. McDonald, D. Jurich, L.M. Duhigg, M. Paniagua, D. Chick, M. Wells, A. Williams, P. Alguire

Academic Medicine: September 2020 - Volume 95 - Issue 9 - p 1388-1395

 

This article aims to assess the correlations between United States Medical Licensing Examination (USMLE) performance, American College of Physicians Internal Medicine In-Training Examination (IM-ITE) performance, American Board of Internal Medicine Internal Medicine Certification Exam (IM-CE) performance, and other medical knowledge and demographic variables.

Posted: | P. Harik, B. E. Clauser, I. Grabovsky, P. Baldwin, M. Margolis, D. Bucak, M. Jodoin, W. Walsh, S. Haist

Journal of Educational Measurement: Volume 55, Issue 2, Pages 308-327

 

The widespread move to computerized test delivery has led to the development of new approaches to evaluating how examinees use testing time and to new metrics designed to provide evidence about the extent to which time limits impact performance. Much of the existing research is based on these types of observational metrics; relatively few studies use randomized experiments to evaluate the impact time limits on scores. Of those studies that do report on randomized experiments, none directly compare the experimental results to evidence from observational metrics to evaluate the extent to which these metrics are able to sensitively identify conditions in which time constraints actually impact scores. The present study provides such evidence based on data from a medical licensing examination.

Posted: | M. von Davier, J. H. Shin, L. Khorramdel, L. Stankov

Applied Psychological Measurement: Volume: 42 issue: 4, page(s): 291-306

 

The research presented in this article combines mathematical derivations and empirical results to investigate effects of the nonparametric anchoring vignette approach proposed by King, Murray, Salomon, and Tandon on the reliability and validity of rating data. The anchoring vignette approach aims to correct rating data for response styles to improve comparability across individuals and groups.