國立高雄大學圖資館 |

登入

回首頁

The multimodal learning analytics handbook

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The multimodal learning analytics handbookedited by Michail Giannakos ... [et al.].
其他作者:	Giannakos, Michail.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	vi, 372 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	EducationData processing.
電子資源:	https://doi.org/10.1007/978-3-031-08076-0
ISBN:	9783031080760$q(electronic bk.)

The multimodal learning analytics handbook
The multimodal learning analytics handbook[electronic resource] /edited by Michail Giannakos ... [et al.]. - Cham :Springer International Publishing :2022. - vi, 372 p. :ill. (some col.), digital ;24 cm.

Introduction to Multimodal Learning Analytics -- Multimodal Learning Analytics and the Design of Learning Spaces -- Multimodal Systems for Automated Oral Presentation Feedback: A Comparative Analysis -- Modeling the Complex Interplay Between Monitoring Events for Regulated Learning with Psychological Networks -- The role of metacognition and self-regulation on clinical reasoning: Leveraging multimodal learning analytics to transform medical education -- Intermodality in Multimodal Learning Analytics for Cognitive Theory Development: A Case from Embodied Design for Mathematics Learning -- Bridging the Gap between Informal Learning Pedagogy and Multimodal Learning Analytics -- Multimodal Learning Experience for Deliberate Practice -- CDM4MMLA: Contextualized Data Model for MultiModal Learning Analytics -- A Physiology-Aware Learning Analytics Framework -- Once more with Feeling - Emotions in Multimodal Learning Analytics -- The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review -- Sensor-based analytics in education: Lessons learned from research in Multimodal Learning Analytics -- Framing the future of Multimodal Learning Analytics.

This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA) The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this "virtual nature" allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated feedback to enable learner self-reflection. However, with this increased complexity in data, new challenges also arise. Conducting the data gathering, pre-processing, analysis, annotation and sense-making, in a way that is meaningful for learning scientists and other stakeholders (e.g. students or teachers), still pose challenges in this emergent field. This handbook aims to serve as a unique resource for state of the art methods and processes. Chapter 11 of this book is available open access under a CC BY 4.0 license at link.springer.com.

ISBN: 9783031080760$q(electronic bk.)

Standard No.: 10.1007/978-3-031-08076-0doiSubjects--Topical Terms:

207353
Education
--Data processing.

LC Class. No.: LB1028.43 / .M85 2022

Dewey Class. No.: 370.285

The multimodal learning analytics handbook
LDR:04393nmm a2200325 a 4500 001 631139
003 DE-He213
005 20221008220828.0
006 m d
007 cr nn 008maaau
008 230411s2022 sz s 0 eng d
020 $a 9783031080760$q(electronic bk.)
020 $a 9783031080753$q(paper)
024 7 $a 10.1007/978-3-031-08076-0 $2 doi
035 $a 978-3-031-08076-0
040 $a GP $c GP
041 0 $a eng
050 4 $a LB1028.43 $b .M85 2022
072 7 $a JNV $2 bicssc
072 7 $a EDU039000 $2 bisacsh
072 7 $a JNV $2 thema
082 0 4 $a 370.285 $2 23
090 $a LB1028.43 $b .M961 2022
245 0 4 $a The multimodal learning analytics handbook $h [electronic resource] / $c edited by Michail Giannakos ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a vi, 372 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction to Multimodal Learning Analytics -- Multimodal Learning Analytics and the Design of Learning Spaces -- Multimodal Systems for Automated Oral Presentation Feedback: A Comparative Analysis -- Modeling the Complex Interplay Between Monitoring Events for Regulated Learning with Psychological Networks -- The role of metacognition and self-regulation on clinical reasoning: Leveraging multimodal learning analytics to transform medical education -- Intermodality in Multimodal Learning Analytics for Cognitive Theory Development: A Case from Embodied Design for Mathematics Learning -- Bridging the Gap between Informal Learning Pedagogy and Multimodal Learning Analytics -- Multimodal Learning Experience for Deliberate Practice -- CDM4MMLA: Contextualized Data Model for MultiModal Learning Analytics -- A Physiology-Aware Learning Analytics Framework -- Once more with Feeling - Emotions in Multimodal Learning Analytics -- The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review -- Sensor-based analytics in education: Lessons learned from research in Multimodal Learning Analytics -- Framing the future of Multimodal Learning Analytics.
520 $a This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA) The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this "virtual nature" allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated feedback to enable learner self-reflection. However, with this increased complexity in data, new challenges also arise. Conducting the data gathering, pre-processing, analysis, annotation and sense-making, in a way that is meaningful for learning scientists and other stakeholders (e.g. students or teachers), still pose challenges in this emergent field. This handbook aims to serve as a unique resource for state of the art methods and processes. Chapter 11 of this book is available open access under a CC BY 4.0 license at link.springer.com.
650 0 $a Education $x Data processing. $3 207353
650 0 $a Educational technology. $3 222132
650 1 4 $a Computers and Education. $3 274532
650 2 4 $a Digital Education and Educational Technology. $3 913216
700 1 $a Giannakos, Michail. $3 877348
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-08076-0
950 $a Computer Science (SpringerNature-11645)