Showing 1 - 5 of 5 Research Library Publications
Posted: | Daniel P. Jurich, Matthew J. Madison

Educational Assessment

 

This study proposes four indices to quantify item influence and distinguishes them from other available item and test measures. We use simulation methods to evaluate and provide guidelines for interpreting each index, followed by a real data application to illustrate their use in practice. We discuss theoretical considerations regarding when influence presents a psychometric concern and other practical concerns such as how the indices function when reducing influence imbalance.

Posted: | Victoria Yaneva, Janet Mee, Le Ha, Polina Harik, Michael Jodoin, Alex Mechaber

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - p 2880–2886

 

This paper presents a corpus of 43,985 clinical patient notes (PNs) written by 35,156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination.

Posted: | P. Harik, R.A. Feinberg RA, B.E. Clauser

Integrating Timing Considerations to Improve Testing Practices

 

This chapter addresses a different aspect of the use of timing data: it provides a framework for understanding how an examinee's use of time interfaces with time limits to impact both test performance and the validity of inferences made based on test scores. It focuses primarily on examinations that are administered as part of the physician licensure process.

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: | J. Salt, P. Harik, M. A. Barone

Academic Medicine: July 2019 - Volume 94 - Issue 7 - p 926-927

 

A response to concerns regarding potential bias in the implementation of machine learning (ML) to scoring of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) patient notes (PN).