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.
Applied Psychological Measurement: Volume 47, issue 1, page(s) 34-47
This study used simulation to investigate the performance of the t-test method in detecting outliers and compared its performance with other outlier detection methods, including the logit difference method with 0.5 and 0.3 as the cutoff values and the robust z statistic with 2.7 as the cutoff value.
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.
Applied Psychological Measurement: Volume: 42 issue: 8, page(s): 595-612
Conventional methods for evaluating the utility of subscores rely on reliability and correlation coefficients. However, correlations can overlook a notable source of variability: variation in subtest means/difficulties. Brennan introduced a reliability index for score profiles based on multivariate generalizability theory, designated as G, which is sensitive to variation in subtest difficulty. However, there has been little, if any, research evaluating the properties of this index. A series of simulation experiments, as well as analyses of real data, were conducted to investigate G under various conditions of subtest reliability, subtest correlations, and variability in subtest means.