Showing 1 - 6 of 6 Research Library Publications
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: | 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, R.A. Feinberg (eds)

Integrating Timing Considerations to Improve Testing Practices

 

This book synthesizes a wealth of theory and research on time issues in assessment into actionable advice for test development, administration, and scoring. 

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

Integrating Timing Considerations to Improve Testing Practices

 

This chapter addresses timing considerations in the context of other types of performance assessments and reports on a previously unpublished experiment examining timing with respect to performance on computer-based case simulations that are used in physician licensure.

Posted: | D. Jurich

Integrating Timing Considerations to Improve Testing Practices

 

This chapter presents a historical overview of the testing literature that exemplifies the theoretical and operational evolution of test speededness.

Posted: | V. Yaneva, L. A. Ha, S. Eraslan, Y. Yesilada, R. Mitkov

IEEE Transactions on Neural Systems and Rehabilitation Engineering

 

The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism.