|
|
|
|
| LEADER |
04870nmm a2200385 u 4500 |
| 001 |
EB002065771 |
| 003 |
EBX01000000000000001205861 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
220901 ||| eng |
| 020 |
|
|
|a 9783031063336
|
| 100 |
1 |
|
|a Wang, Yuan 'Elle'
|e [editor]
|
| 245 |
0 |
0 |
|a Social and Emotional Learning and Complex Skills Assessment
|h Elektronische Ressource
|b An Inclusive Learning Analytics Perspective
|c edited by Yuan 'Elle' Wang, Srećko Joksimović, Maria Ofelia Z. San Pedro, Jason D. Way, John Whitmer
|
| 250 |
|
|
|a 1st ed. 2022
|
| 260 |
|
|
|a Cham
|b Springer International Publishing
|c 2022, 2022
|
| 300 |
|
|
|a XVI, 335 p. 55 illus., 38 illus. in color
|b online resource
|
| 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 LearningUsing 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)
|
| 653 |
|
|
|a Education / Data processing
|
| 653 |
|
|
|a Digital Education and Educational Technology
|
| 653 |
|
|
|a Computers and Education
|
| 653 |
|
|
|a Data mining
|
| 653 |
|
|
|a Educational technology
|
| 653 |
|
|
|a Data Mining and Knowledge Discovery
|
| 700 |
1 |
|
|a Joksimović, Srećko
|e [editor]
|
| 700 |
1 |
|
|a San Pedro, Maria Ofelia Z.
|e [editor]
|
| 700 |
1 |
|
|a Way, Jason D.
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Advances in Analytics for Learning and Teaching
|
| 028 |
5 |
0 |
|a 10.1007/978-3-031-06333-6
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-031-06333-6?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 371.33
|
| 520 |
|
|
|a 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.
|
| 520 |
|
|
|a 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)
|
| 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.
|