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.
Teaching and Learning in Medicine: Volume 33 - Issue 4 - p 366-381
The purpose of this analysis is to describe these sources of evidence that can be used to evaluate the quality of generated items. The important role of medical expertise in the development and evaluation of the generated items is highlighted as a crucial requirement for producing validation evidence.
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.
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.