東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Computational learning theories = mo...

Gibson, David C.

Linked to FindBook

Google Book

Amazon

博客來

Computational learning theories = models for artificial intelligence promoting learning processes /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational learning theories/ by David C. Gibson, Dirk Ifenthaler.
Reminder of title:	models for artificial intelligence promoting learning processes /
Author:	Gibson, David C.
other author:	Ifenthaler, Dirk.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xiii, 154 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Why 'Computational' Learning Theories? -- 2. AI and Learning Processes -- 3. A Complex Hierarchical Framework of Learning -- 4. Piaget and the Ontogeny of Intelligence -- 5. Keller and the ARCS Model of Motivation -- 6. Complexity Theory and Learning -- 7. AI Roles for Enhancing Individual Learning -- 8. Informal Social Learning -- 9. How People Learn -- 10. AI Assisting Individuals as Team Members -- 11. AI Roles for the Team or Organization -- 12. A Network Theory of Culture -- 13. AI Roles in Cultural Learning -- 14. Open Questions.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Educational applications. -
Online resource:	https://doi.org/10.1007/978-3-031-65898-3
ISBN:	9783031658983

Computational learning theories = models for artificial intelligence promoting learning processes /
Gibson, David C.

Computational learning theoriesmodels for artificial intelligence promoting learning processes /[electronic resource] :by David C. Gibson, Dirk Ifenthaler. - Cham :Springer Nature Switzerland :2024. - xiii, 154 p. :ill., digital ;24 cm. - Advances in analytics for learning and teaching,2662-2130. - Advances in analytics for learning and teaching..

1. Why 'Computational' Learning Theories? -- 2. AI and Learning Processes -- 3. A Complex Hierarchical Framework of Learning -- 4. Piaget and the Ontogeny of Intelligence -- 5. Keller and the ARCS Model of Motivation -- 6. Complexity Theory and Learning -- 7. AI Roles for Enhancing Individual Learning -- 8. Informal Social Learning -- 9. How People Learn -- 10. AI Assisting Individuals as Team Members -- 11. AI Roles for the Team or Organization -- 12. A Network Theory of Culture -- 13. AI Roles in Cultural Learning -- 14. Open Questions.

This book shows how artificial intelligence grounded in learning theories can promote individual learning, team productivity and multidisciplinary knowledge-building. It advances the learning sciences by integrating learning theory with computational biology and complexity, offering an updated mechanism of learning, which integrates previous theories, provides a basis for scaling from individuals to societies, and unifies models of psychology, sociology and cultural studies. The book provides a road map for the development of AI that addresses the central problems of learning theory in the age of artificial intelligence including: optimizing human-machine collaboration promoting individual learning balancing personalization with privacy dealing with biases and promoting fairness explaining decisions and recommendations to build trust and accountability continuously balancing and adapting to individual, team and organizational goals generating and generalizing knowledge across fields and domains The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.

ISBN: 9783031658983

Standard No.: 10.1007/978-3-031-65898-3doiSubjects--Topical Terms:

567577
Artificial intelligence
--Educational applications.

LC Class. No.: LB1028.43

Dewey Class. No.: 371.33463

Computational learning theories = models for artificial intelligence promoting learning processes /
LDR:02803nmm a2200337 a 4500 001 2374096
003 DE-He213
005 20240716125225.0
006 m d
007 cr nn 008maaau
008 241231s2024 sz s 0 eng d
020 $a 9783031658983 $q (electronic bk.)
020 $a 9783031658976 $q (paper)
024 7 $a 10.1007/978-3-031-65898-3 $2 doi
035 $a 978-3-031-65898-3
040 $a GP $c GP
041 0 $a eng
050 4 $a LB1028.43
072 7 $a JN $2 bicssc
072 7 $a EDU037000 $2 bisacsh
072 7 $a JN $2 thema
082 0 4 $a 371.33463 $2 23
090 $a LB1028.43 $b .G448 2024
100 1 $a Gibson, David C. $3 3722715
245 1 0 $a Computational learning theories $h [electronic resource] : $b models for artificial intelligence promoting learning processes / $c by David C. Gibson, Dirk Ifenthaler.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xiii, 154 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in analytics for learning and teaching, $x 2662-2130
505 0 $a 1. Why 'Computational' Learning Theories? -- 2. AI and Learning Processes -- 3. A Complex Hierarchical Framework of Learning -- 4. Piaget and the Ontogeny of Intelligence -- 5. Keller and the ARCS Model of Motivation -- 6. Complexity Theory and Learning -- 7. AI Roles for Enhancing Individual Learning -- 8. Informal Social Learning -- 9. How People Learn -- 10. AI Assisting Individuals as Team Members -- 11. AI Roles for the Team or Organization -- 12. A Network Theory of Culture -- 13. AI Roles in Cultural Learning -- 14. Open Questions.
520 $a This book shows how artificial intelligence grounded in learning theories can promote individual learning, team productivity and multidisciplinary knowledge-building. It advances the learning sciences by integrating learning theory with computational biology and complexity, offering an updated mechanism of learning, which integrates previous theories, provides a basis for scaling from individuals to societies, and unifies models of psychology, sociology and cultural studies. The book provides a road map for the development of AI that addresses the central problems of learning theory in the age of artificial intelligence including: optimizing human-machine collaboration promoting individual learning balancing personalization with privacy dealing with biases and promoting fairness explaining decisions and recommendations to build trust and accountability continuously balancing and adapting to individual, team and organizational goals generating and generalizing knowledge across fields and domains The book will be of interest to educational professionals, researchers, and developers of educational technology that utilize artificial intelligence.
650 0 $a Artificial intelligence $x Educational applications. $3 567577
650 1 4 $a Research Methods in Education. $3 3219447
650 2 4 $a Digital Education and Educational Technology. $3 3538616
650 2 4 $a Educational Psychology. $3 1005868
700 1 $a Ifenthaler, Dirk. $3 901275
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in analytics for learning and teaching. $3 3451185
856 4 0 $u https://doi.org/10.1007/978-3-031-65898-3
950 $a Education (SpringerNature-41171)