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.
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.
Adv in Health Sci Educ 24, 141–150 (2019)
Research suggests that the three-option format is optimal for multiple choice questions (MCQs). This conclusion is supported by numerous studies showing that most distractors (i.e., incorrect answers) are selected by so few examinees that they are essentially nonfunctional. However, nearly all studies have defined a distractor as nonfunctional if it is selected by fewer than 5% of examinees.