Duanli Yan – författare

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

As a sequel to the widely acclaimed Computerized Multistage Testing: Theory and Applications (2014) by Yan, von Davier, and Lewis, this volume delves into the experiences, considerations, challenges, and lessons learned over the past years. It also offers practical approaches and solutions to the issues encountered. The topics covered include purposeful MST designs, practical approaches for optimal design, assembly strategies for accuracy and efficiency, hybrid designs, MST with natural language processing, practical routing considerations and methodologies, item calibration and proficiency estimation methods, routing and classification accuracy, added value of process data, prediction and evaluation of MST performance, cognitive diagnostic MST, differential item functioning, robustness of statistical methods, simulations, test security, the new digital large-scale Scholastic Aptitude Test, software for practical assessment and simulations, artificial intelligence impact, and the future of adaptive MST.

This volume is intended for students, faculty, researchers, practitioners, and education officers in the fields of educational measurement and evaluation in the United States and internationally.

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

As a sequel to the widely acclaimed Computerized Multistage Testing: Theory and Applications (2014) by Yan, von Davier, and Lewis, this volume delves into the experiences, considerations, challenges, and lessons learned over the past years. It also offers practical approaches and solutions to the issues encountered. The topics covered include purposeful MST designs, practical approaches for optimal design, assembly strategies for accuracy and efficiency, hybrid designs, MST with natural language processing, practical routing considerations and methodologies, item calibration and proficiency estimation methods, routing and classification accuracy, added value of process data, prediction and evaluation of MST performance, cognitive diagnostic MST, differential item functioning, robustness of statistical methods, simulations, test security, the new digital large-scale Scholastic Aptitude Test, software for practical assessment and simulations, artificial intelligence impact, and the future of adaptive MST.

This volume is intended for students, faculty, researchers, practitioners, and education officers in the fields of educational measurement and evaluation in the United States and internationally.

Gathering contributions from leading scholars around the world, this handbook offers a comprehensive resource on the most recent advances in research surrounding the theories, methodologies, and applications of science learning progressions.

Researchers and educators have used learning progressions to guide the design and alignment of curriculum, instruction, and assessment, and to help students learn scientific knowledge and practices in a coherent and connected way across multiple years. This handbook lays out the development and current state of research in this field across four sections: learning progression theories and methodologies; learning progressions to promote student learning; teachers’ learning and use of learning progressions; and new technology in learning progression research.

Featuring internationally-recognized experts in learning progression research as well as up-and-coming voices, the Handbook of Research on Science Learning Progressions offers a defining new resource for researchers, teachers and teacher educators, and curriculum and assessment developers in science education.

Gathering contributions from leading scholars around the world, this handbook offers a comprehensive resource on the most recent advances in research surrounding the theories, methodologies, and applications of science learning progressions.

Researchers and educators have used learning progressions to guide the design and alignment of curriculum, instruction, and assessment, and to help students learn scientific knowledge and practices in a coherent and connected way across multiple years. This handbook lays out the development and current state of research in this field across four sections: learning progression theories and methodologies; learning progressions to promote student learning; teachers’ learning and use of learning progressions; and new technology in learning progression research.

Featuring internationally-recognized experts in learning progression research as well as up-and-coming voices, the Handbook of Research on Science Learning Progressions offers a defining new resource for researchers, teachers and teacher educators, and curriculum and assessment developers in science education.

"Automated scoring engines […] require a careful balancing of the contributions of technology, NLP, psychometrics, artificial intelligence, and the learning sciences. The present handbook is evidence that the theories, methodologies, and underlying technology that surround automated scoring have reached maturity, and that there is a growing acceptance of these technologies among experts and the public."

From the Foreword by Alina von Davier, ACTNext Senior Vice President

Handbook of Automated Scoring: Theory into Practice provides a scientifically grounded overview of the key research efforts required to move automated scoring systems into operational practice. It examines the field of automated scoring from the viewpoint of related scientific fields serving as its foundation, the latest developments of computational methodologies utilized in automated scoring, and several large-scale real-world applications of automated scoring for complex learning and assessment systems. The book is organized into three parts that cover (1) theoretical foundations, (2) operational methodologies, and (3) practical illustrations, each with a commentary. In addition, the handbook includes an introduction and synthesis chapter as well as a cross-chapter glossary.

"Automated scoring engines […] require a careful balancing of the contributions of technology, NLP, psychometrics, artificial intelligence, and the learning sciences. The present handbook is evidence that the theories, methodologies, and underlying technology that surround automated scoring have reached maturity, and that there is a growing acceptance of these technologies among experts and the public."

From the Foreword by Alina von Davier, ACTNext Senior Vice President

Handbook of Automated Scoring: Theory into Practice provides a scientifically grounded overview of the key research efforts required to move automated scoring systems into operational practice. It examines the field of automated scoring from the viewpoint of related scientific fields serving as its foundation, the latest developments of computational methodologies utilized in automated scoring, and several large-scale real-world applications of automated scoring for complex learning and assessment systems. The book is organized into three parts that cover (1) theoretical foundations, (2) operational methodologies, and (3) practical illustrations, each with a commentary. In addition, the handbook includes an introduction and synthesis chapter as well as a cross-chapter glossary.

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments.

Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD,situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics.

This book is both a resource for professionals interested in assessment and advanced students.  Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.