Showing 1 - 8 of 8 Research Library Publications
Posted: | King Yiu Suen, Victoria Yaneva, Le An Ha, Janet Mee, Yiyun Zhou, Polina Harik

Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), Pages 443-447

 

This paper presents the ACTA system, which performs automated short-answer grading in the domain of high-stakes medical exams. The system builds upon previous work on neural similarity-based grading approaches by applying these to the medical domain and utilizing contrastive learning as a means to optimize the similarity metric. 

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: | Daniel Jurich, Michelle Daniel, Karen E. Hauer, Christine Seibert, Latha Chandran, Arnyce R. Pock, Sara B. Fazio, Amy Fleming, Sally A. Santen

Teaching and Learning in Medicine: Volume 33 - Issue 4 - p 366-381

 

CSE scores for students from eight schools that moved Step 1 after core clerkships between 2012 and 2016 were analyzed in a pre-post format. Hierarchical linear modeling was used to quantify the effect of the curriculum on CSE performance. Additional analysis determined if clerkship order impacted clinical subject exam performance and whether the curriculum change resulted in more students scoring in the lowest percentiles before and after the curricular change.

Posted: | M. J. Margolis, B. E. Clauser

Handbook of Automated Scoring

 

In this chapter we describe the historical background that led to development of the simulations and the subsequent refinement of the construct that occurred as the interface was being developed. We then describe the evolution of the automated scoring procedures from linear regression modeling to rule-based procedures.

Posted: | B.C. Leventhal, I. Grabovsky

Educational Measurement: Issues and Practice, 39: 30-36

 

This article proposes the conscious weight method and subconscious weight method to bring more objectivity to the standard setting process. To do this, these methods quantify the relative harm of the negative consequences of false positive and false negative misclassification.

Posted: | E. C. Carey, M. Paniagua, L. J. Morrison, S. K. Levine, J. C. Klick, G. T. Buckholz, J. Rotella, J. Bruno, S. Liao, R. M. Arnold

Journal of Pain and Symptom Management: Volume 56, Issue 3, p371-378

 

This article reviews the USMLE step examinations to determine whether they test the palliative care (PC) knowledge necessary for graduating medical students and residents applying for licensure.

Posted: | R.A. Feinberg, D. Jurich, J. Lord, H. Case, J. Hawley

Journal of Veterinary Medical Education 2018 45:3, 381-387

 

This study uses item response data from the November–December 2014 and April 2015 NAVLE administrations (n =5,292), to conduct timing analyses comparing performance across several examinee subgroups. The results provide evidence that conditions were sufficient for most examinees, thereby supporting the current time limits. For the relatively few examinees who may have been impacted, results suggest the cause is not a bias with the test but rather the effect of poor pacing behavior combined with knowledge deficits.