This book examines the types of web-based testing applications that exist, their technical requirements, and their acceptability and use in various countries.

The use of technology for workplace and occupational testing blossomed in the early years of this century. This book offers a demonstration that the first generation of these technologies have now been implemented long enough to observe the patterns and issues that emerge when these approaches evolve through technical advancement and successive application. A new set of issues and opportunities has emerged and the next generation of these applications is now coming of age. This book reflects on the last few decades of this evolutionary process from a vantage point of global experience across a wide range of workplace applications, including employment selection, development, and occupational certification. The themes and issues that arise as this broad treatment unfolds provide an essential foundation for students, researchers, and professionals who are involved with the assessment of human capability and potential in organizational and workplace contexts

The book provides practical explanation and guidance to nontechnical readers who want to expand the range of assessments used in schools, beyond multiple choice tests to newer, more experimental measures that can provide a fuller portrait of students' interests, capacities, and learning.--

This book constitutes the proceedings of the 21st International Conference on Technology Enhanced Assessment, TEA 2018, held in Amsterdam, The Netherlands, in December 2018. The 14 papers presented were carefully selected from 34 submissions. They are centered around topics like e-learning, computer-assisted instruction, interactive learning environments, collaborative learning, computing education, student assessment.

This book examines scholarship, best practice methodologies, and examples of policy and practice from various professional fields in education and psychology to illuminate the elevated emphasis on test fairness in the 2014 Standards for Educational and Psychological Testing. Together, the chapters provide a survey of critical and current issues with a view to broadening and contextualizing the fairness guidelines for different types of tests, test takers, and testing contexts. Researchers and practitioners from school psychology, clinical/counseling psychology, industrial/organizational psychology, and education will find the content useful in thinking more acutely about fairness in testing in their work. The book also has chapters that address implications for policy makers, and, in some cases, the public. These discussions are offered as a starting point for future scholarship on the theoretical, empirical, and applied aspects of fairness in testing particularly given the ever-increasing importance of addressing equity in testing.

Education is a hot topic. From the stage of presidential debates to tonight's dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn't work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education. The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective? At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning. Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored. With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.