robert mislevy (31 Ergebnisse)

Hardcover. Zustand: Brand New. 2nd edition. 335 pages. 9.25x6.50x1.25 inches. In Stock.

Paperback. Zustand: Brand New. 437 pages. 10.00x7.00x1.00 inches. In Stock.

Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. Ex library copy with usual stamps & stickers.

Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Soft Cover. Zustand: Acceptable. Ex-library with the usual features. A few pages have a small ink stain at lower edge, otherwise the interior is clean and tight. Binding is good. Cover shows light wear. 427 pages.

Hardcover. Zustand: Brand New. 272 pages. 9.25x6.10x0.71 inches. In Stock.

Taschenbuch. Zustand: Neu. Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment | With Examples in R and Python | Alina A. Von Davier (u. a.) | Taschenbuch | x | Englisch | 2022 | Springer | EAN 9783030743963 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Hardcover. Zustand: Sehr gut. leichte Druckstelle am Cover u. Cover etwas berieben, kleine Lagerspuren am Buch, Inhalt einwandfrei und ungelesen 124134 Sprache: Englisch Gewicht in Gramm: 760.

Paperback. Zustand: Brand New. reprint edition. 695 pages. 9.25x6.10x1.50 inches. In Stock.

Zustand: New. pp. 416.

Paperback. Zustand: Brand New. 492 pages. 10.00x7.01x1.57 inches. In Stock.

Paperback. Zustand: Brand New. 416 pages. 9.00x6.00x1.00 inches. In Stock.

Paperback. Zustand: Brand New. 130 pages. 9.00x6.00x0.25 inches. In Stock.

Paperback. Zustand: Brand New. 1st edition. 427 pages. 9.00x6.00x1.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Assessing Model-Based Reasoning using Evidence- Centered Design | A Suite of Research-Based Design Patterns | Robert J. Mislevy (u. a.) | Taschenbuch | xvii | Englisch | 2017 | Springer | EAN 9783319522456 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.

Zustand: New. pp. 416.

Zustand: New. pp. xxiii + 335 Illus.

Zustand: New.

Hardcover. Zustand: Brand New. 1st edition. 492 pages. 10.20x7.20x1.22 inches. In Stock.

Zustand: New. pp. 416.

Hardcover. Zustand: Brand New. 2015 edition. 662 pages. 9.25x6.25x1.50 inches. In Stock.

Hard Cover. Zustand: Acceptable. No Jacket. Ex-library with the usual features. Front hinge has a small crack at upper edge. The interior is clean and tight. Cover shows light wear and has a tiny tear at lower edge of spine. 666 pages. Ex-Library.

Zustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Buch. Zustand: Neu. Neuware -This book defines and describes a new discipline, named ¿computational psychometrics,¿ from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners¿ performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks.Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term ¿computational¿ has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, ¿computational¿ has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community.In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book defines and describes a new discipline, named 'computational psychometrics,' from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners' performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks.Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term 'computational' has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, 'computational' has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community.In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book defines and describes a new discipline, named 'computational psychometrics,' from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners' performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks.Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term 'computational' has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, 'computational' has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community.In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.

Zustand: Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Hardcover. Zustand: Brand New. 1st edition. 416 pages. 9.25x6.00x1.50 inches. In Stock.

Hardcover. Zustand: Brand New. 1st edition. 427 pages. 9.00x6.25x1.25 inches. In Stock.