Showing 1 - 6 of 6 Research Library Publications
Posted: | Victoria Yaneva (editor), Matthias von Davier (editor)

Advancing Natural Language Processing in Educational Assessment

 

This book examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond.

Posted: | Martin G. Tolsgaard, Martin V. Pusic, Stefanie S. Sebok-Syer, Brian Gin, Morten Bo Svendsen, Mark D. Syer, Ryan Brydges, Monica M. Cuddy, Christy K. Boscardin

Medical Teacher: Volume 45 - Issue 6, Pages 565-573

 

This guide aims aim to describe practical considerations involved in reading and conducting studies in medical education using Artificial Intelligence (AI), define basic terminology and identify which medical education problems and data are ideally-suited for using AI.

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.

Posted: | Martin G. Tolsgaard, Christy K. Boscardin, Yoon Soo Park, Monica M. Cuddy, Stefanie S. Sebok-Syer

Advances in Health Sciences Education: Volume 25, p 1057–1086 (2020)

 

This critical review explores: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice.

Posted: | Kimberly Hu, Patricia J. Hicks, Melissa Margolis, Carol Carraccio, Amanda Osta, Marcia L. Winward, Alan Schwartz

Academic Medicine: Volume 95 - Issue 11S - Pages S89-S94

 

Semiannually, U.S. pediatrics residency programs report resident milestone levels to the Accreditation Council for Graduate Medical Education (ACGME). The Pediatrics Milestones Assessment Collaborative (PMAC) developed workplace-based assessments of 2 inferences. The authors compared learner and program variance in PMAC scores with ACGME milestones.

Posted: | P.J. Hicks, M.J. Margolis, C.L. Carraccio, B.E. Clauser, K. Donnelly, H.B. Fromme, K.A. Gifford, S.E. Poynter, D.J. Schumacher, A. Schwartz & the PMAC Module 1 Study Group

Medical Teacher: Volume 40 - Issue 11 - p 1143-1150

 

This study explores a novel milestone-based workplace assessment system that was implemented in 15 pediatrics residency programs. The system provided: web-based multisource feedback and structured clinical observation instruments that could be completed on any computer or mobile device; and monthly feedback reports that included competency-level scores and recommendations for improvement.