RESEARCH LIBRARY

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.

Patient Education and Counseling: Volume 109, Supplement, April 2023, Page 2

Physicians' responses to patient communication were assessed by both clinically matched and unmatched analogue patients (APs). Significant correlations between their ratings indicated consistency in evaluating physician communication skills. Thematic analysis identified twenty-one common themes in both clinically matched and unmatched AP responses, suggesting similar assessments of important behaviors. These findings imply that clinically unmatched APs can effectively substitute for clinically matched ones in evaluating physician communication and offering feedback when the latter are unavailable.

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. 

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.

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.