Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment (Record no. 105610)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06089nam a22005655i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-030-74394-9 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MN-UlMNUE |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230202140154.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 211213s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030743949 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-030-74394-9 |
| Source of number or code | doi |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MN-UlMNUE |
| Language of cataloging | English |
| Transcribing agency | MN-UlMNUE |
| Modifying agency | MN-UlMNUE |
| Description conventions | rda |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | L1-991 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | JN |
| Source | bicssc |
| Subject category code | EDU011000 |
| Source | bisacsh |
| Subject category code | JN |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 370 |
| Edition number | 23 |
| 245 10 - TITLE STATEMENT | |
| Title | Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment |
| Medium | [electronic resource] : |
| Remainder of title | With Examples in R and Python / |
| Statement of responsibility, etc. | edited by Alina A. von Davier, Robert J. Mislevy, Jiangang Hao. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing : |
| -- | Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | X, 262 p. 1 illus. |
| Other physical details | online resource. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | text file |
| Encoding format | |
| Source | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Methodology of Educational Measurement and Assessment, |
| International Standard Serial Number | 2367-1718 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1. Introduction. Computational Psychometrics: Towards a Principled Integration of Data Science and Machine Learning Techniques into Psychometrics (Alina A. von Davier, Robert Mislevy and Jiangang Hao) -- Part I. Conceptualization. 2. Next generation learning and assessment: what, why and how (Robert Mislevy) -- 3. Computational psychometrics (Alina A. von Davier, Kristen DiCerbo and Josine Verhagen) -- 4. Virtual performance-based assessments (Jessica Andrews-Todd, Robert Mislevy, Michelle LaMar and Sebastiaan de Klerk) -- 5. Knowledge Inference Models Used in Adaptive Learning (Maria Ofelia Z. San Pedro and Ryan S. Baker) -- Part II. Methodology. 6. Concepts and models from Psychometrics (Robert Mislevy and Maria Bolsinova) -- 7. Bayesian Inference in Large-Scale Computational Psychometrics (Gunter Maris, Timo Bechger and Maarten Marsman) -- 8. Data science perspectives (Jiangang Hao and Robert Mislevy) -- 9. Supervised machine learning (Jiangang Hao) -- 10. Unsupervised machine learning (Pak Chunk Wong) -- 11. AI and deep learning for educational research (Yuchi Huang and Saad M. Khan) -- 12. Time series and stochastic processes (Peter Halpin, Lu Ou and Michelle LaMar) -- 13. Social network analysis (Mengxiao Zhu) -- 14. Text mining and automated scoring (Michael Flor and Jiangang Hao). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Education. |
| Topical term or geographic name entry element | Psychometrics. |
| 9 (RLIN) | 2702 |
| Topical term or geographic name entry element | Social sciences—Statistical methods. |
| 9 (RLIN) | 2070 |
| Topical term or geographic name entry element | Education. |
| 9 (RLIN) | 2911 |
| Topical term or geographic name entry element | Psychometrics. |
| 9 (RLIN) | 2702 |
| Topical term or geographic name entry element | Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. |
| 9 (RLIN) | 2071 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | von Davier, Alina A. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 9 (RLIN) | 2704 |
| Personal name | Mislevy, Robert J. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 9 (RLIN) | 2705 |
| Personal name | Hao, Jiangang. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 9 (RLIN) | 2706 |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783030743932 |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783030743956 |
| Relationship information | Printed edition: |
| International Standard Book Number | 9783030743963 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Methodology of Educational Measurement and Assessment, |
| International Standard Serial Number | 2367-1718 |
| 9 (RLIN) | 2707 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-030-74394-9 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Цахим хувилбартай гадаад ном |
No items available.