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Educational tests and measurements --- Academic achievement --- Evaluation.

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Test bias. --- Examinations --- Test results --- Test validity --- Validity of examinations --- Bias in tests --- Prejudice in testing --- Discrimination in education --- Educational tests and measurements --- Validity. --- Interpretation --- Validity --- Test fairness

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"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" --

Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- Examinations --- Educational tests and measurements --- Artificial intelligence --- Computer adaptive testing. --- Design and construction. --- Data processing. --- Educational applications. --- Adaptive testing, Computer --- CAT (Computer adaptive testing) --- Computer adaptive tests --- Computerized adaptive testing --- Ability --- Competency-based educational tests --- Education --- Test construction --- Test design --- Testing --- Data processing

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"The general theme of this book is to present the innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for the learning analytics. The first two chapters focused on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrated the use of network analysis for learning analytics. Chapter 6 introduced the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrated the usage of generalized linear mixed effect models and Item Tree Models to analyze eye-tracking data. Chapter 8 discussed the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrated the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduced innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. 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 model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing the new data sources in order to better provide diagnostic information about the students. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age"--

Educational evaluation. --- Psychometrics. --- Psychometrics

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"This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and/or software are included. Chapter 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and/or achieve more efficient instructions from various sources of data. Chapter 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system. In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes"--

Educational evaluation --- Educational indicators. --- Effective teaching. --- Data processing.