000			06089nam a22005655i 4500
001			978-3-030-74394-9
003			MN-UlMNUE
005			20230202140154.0
007			cr nn 008mamaa
008			211213s2021 sz | s |||| 0|eng d
020			_a9783030743949
024	7		_a10.1007/978-3-030-74394-9 _2doi
040			_aMN-UlMNUE _bENG _cMN-UlMNUE _dMN-UlMNUE _erda
041			_aENG
050		4	_aL1-991
072		7	_aJN _2bicssc
072		7	_aEDU011000 _2bisacsh
072		7	_aJN _2thema
082	0	4	_a370 _223
245	1	0	_aComputational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment _h[electronic resource] : _bWith Examples in R and Python / _cedited by Alina A. von Davier, Robert J. Mislevy, Jiangang Hao.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aX, 262 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aMethodology of Educational Measurement and Assessment, _x2367-1718
505	0		_a1. 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			_aThis 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	_aEducation.
650		0	_aPsychometrics. _92702
650		0	_aSocial sciences—Statistical methods. _92070
650	1	4	_aEducation. _92911
650	2	4	_aPsychometrics. _92702
650	2	4	_aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. _92071
700	1		_avon Davier, Alina A. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _92704
700	1		_aMislevy, Robert J. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _92705
700	1		_aHao, Jiangang. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _92706
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030743932
776	0	8	_iPrinted edition: _z9783030743956
776	0	8	_iPrinted edition: _z9783030743963
830		0	_aMethodology of Educational Measurement and Assessment, _x2367-1718 _92707
856	4	0	_uhttps://doi.org/10.1007/978-3-030-74394-9
942			_2ddc _cEBOOK
999			_c105610 _d105610