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.
Educational Measurement: Issues and Practice
This short, invited manuscript focuses on the implications for certification and licensure assessment organizations as a result of the wide‐spread disruptions caused by the COVID-19 pandemic.
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.
Measurement: Interdisciplinary Research and Perspectives, 16:1, 59-70
This article critically reviews how diagnostic models have been conceptualized and how they compare to other approaches used in educational measurement. In particular, certain assumptions that have been taken for granted and used as defining characteristics of diagnostic models are reviewed and it is questioned whether these assumptions are the reason why these models have not had the success in operational analyses and large-scale applications, contrary to what many have hoped.