國立高雄大學圖資館 |

Social and emotional learning and complex skills assessmentan inclusive learning analytics perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Social and emotional learning and complex skills assessmentedited by Yuan 'Elle' Wang ... [et al.].
Reminder of title:	an inclusive learning analytics perspective /
other author:	Wang, Yuan.
Published:	Cham :Springer International Publishing :2022.
Description:	xvi, 335 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Social learningEvaluation.
Online resource:	https://doi.org/10.1007/978-3-031-06333-6
ISBN:	9783031063336$q(electronic bk.)

Social and emotional learning and complex skills assessmentan inclusive learning analytics perspective /
Social and emotional learning and complex skills assessmentan inclusive learning analytics perspective /[electronic resource] :edited by Yuan 'Elle' Wang ... [et al.]. - Cham :Springer International Publishing :2022. - xvi, 335 p. :ill., digital ;24 cm. - Advances in analytics for learning and teaching,2662-2130. - Advances in analytics for learning and teaching..

Re-contextualizing inclusiveness & SEL in Learning Analytics -- State of the science on social and emotional learning: Frameworks, assessment, and developing Skills -- Mapping the landscape of social and emotional learning analytics -- Empathy: Is Technology Strengthening or Fostering its Decline in the 21st Century? -- Creativity and Industry 4.0 -- Using Learning Analytics to Measure Motivational and Affective Processes in SRL -- A typology of self-regulation in writing from multiple sources -- Investigating the educators' needs and interpretations of the collaboration process analytics -- Augmented Reality (AR) for Biology Learning: A Quasi-experiment Study with High School Students -- Struggling Readers Smiling on the Inside and Getting Correct Answers -- Exploring Selective College Attendance and Middle School Cognitive and Non-Cognitive Factors within Computer-Based Math Learning -- Supporting Doctoral Student Social-Emotional Learning Using Single-Case Learning Analytics -- Investigating the Potential of AI-based Social Matching Systems to Facilitate Social Interaction Among Online Learners -- Developing Social Interaction Metrics for an Active, Social, and Case-Based Online Learning Platform -- Network Climate Action through MOOCs Cornell (Environmental education)

In this book, we primarily focus on studies that provide objective, unobtrusive, and innovative measures (e.g., indirect measures, content analysis, or analysis of trace data) of SEL skills (e.g., collaboration, creativity, persistence), relying primarily on learning analytics methods and approaches that would potentially allow for expanding the assessment of SEL skills and competencies at scale. What makes the position of learning analytics pivotal in this endeavor to redefine measurement of SEL skills are constant changes and advancements in learning environments and the quality and quantity of data collected about learners and the process of learning. Contemporary learning environments that utilize virtual and augmented reality to enhance learning opportunities accommodate for designing tasks and activities that allow learners to elicit behaviors (either in face-to-face or online context) not being captured in traditional educational settings. Novel insights provided in the book span across diverse types of learning contexts and learner populations. Specifically, the book addresses relevant and emerging theories and frameworks (in various disciplines such as education, psychology, or workforce) that inform assessments of SEL skills and competencies. In so doing, the book maps the landscape of the novel learning analytics methods and approaches, along with their application in the SEL assessment for K-12 learners as well as adult learners. Critical to the notion of the SEL assessment are data sources. In that sense, the book outlines where and how data related to learners' 21st century skills and competencies can be measured and collected. Linking theory to data, the book further discusses tools and methods that are being used to operationalize SEL and link relevant skills and competencies with cognitive assessment. Finally, the book addresses aspects of generalizability and applicability, showing promising approaches for translating research findings into actionable insights that would inform various stakeholders (e.g., learners, instructors, administrators, policy makers)

ISBN: 9783031063336$q(electronic bk.)

Standard No.: 10.1007/978-3-031-06333-6doiSubjects--Topical Terms:

933639
Social learning
--Evaluation.

LC Class. No.: LC192.4 / .S63 2022

Dewey Class. No.: 370.115

Social and emotional learning and complex skills assessmentan inclusive learning analytics perspective /
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505 0 $a Re-contextualizing inclusiveness & SEL in Learning Analytics -- State of the science on social and emotional learning: Frameworks, assessment, and developing Skills -- Mapping the landscape of social and emotional learning analytics -- Empathy: Is Technology Strengthening or Fostering its Decline in the 21st Century? -- Creativity and Industry 4.0 -- Using Learning Analytics to Measure Motivational and Affective Processes in SRL -- A typology of self-regulation in writing from multiple sources -- Investigating the educators' needs and interpretations of the collaboration process analytics -- Augmented Reality (AR) for Biology Learning: A Quasi-experiment Study with High School Students -- Struggling Readers Smiling on the Inside and Getting Correct Answers -- Exploring Selective College Attendance and Middle School Cognitive and Non-Cognitive Factors within Computer-Based Math Learning -- Supporting Doctoral Student Social-Emotional Learning Using Single-Case Learning Analytics -- Investigating the Potential of AI-based Social Matching Systems to Facilitate Social Interaction Among Online Learners -- Developing Social Interaction Metrics for an Active, Social, and Case-Based Online Learning Platform -- Network Climate Action through MOOCs Cornell (Environmental education)
520 $a In this book, we primarily focus on studies that provide objective, unobtrusive, and innovative measures (e.g., indirect measures, content analysis, or analysis of trace data) of SEL skills (e.g., collaboration, creativity, persistence), relying primarily on learning analytics methods and approaches that would potentially allow for expanding the assessment of SEL skills and competencies at scale. What makes the position of learning analytics pivotal in this endeavor to redefine measurement of SEL skills are constant changes and advancements in learning environments and the quality and quantity of data collected about learners and the process of learning. Contemporary learning environments that utilize virtual and augmented reality to enhance learning opportunities accommodate for designing tasks and activities that allow learners to elicit behaviors (either in face-to-face or online context) not being captured in traditional educational settings. Novel insights provided in the book span across diverse types of learning contexts and learner populations. Specifically, the book addresses relevant and emerging theories and frameworks (in various disciplines such as education, psychology, or workforce) that inform assessments of SEL skills and competencies. In so doing, the book maps the landscape of the novel learning analytics methods and approaches, along with their application in the SEL assessment for K-12 learners as well as adult learners. Critical to the notion of the SEL assessment are data sources. In that sense, the book outlines where and how data related to learners' 21st century skills and competencies can be measured and collected. Linking theory to data, the book further discusses tools and methods that are being used to operationalize SEL and link relevant skills and competencies with cognitive assessment. Finally, the book addresses aspects of generalizability and applicability, showing promising approaches for translating research findings into actionable insights that would inform various stakeholders (e.g., learners, instructors, administrators, policy makers)
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