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).
Author Biography:
Dr. Elle Yuan Wang is a Lead Research and Data Scientist at ASU EdPlus Action Lab and the National AI Institute of Adult Learning and Online Education (AI-ALOE). Her current projects center on assessing social and emotional leaning skillsets and predicting learner longitudinal career development in large-scale online learning environments in AI-augmented learning environments. Specifically, her projects take a comprehensive approach by linking three sources of learner data: pre-course learner motivation, within-course learner engagement, as well as post-course development. She obtained a Ph.D in Cognitive Sciences from Columbia University and has led various projects funded by the National Science Foundation (NSF), America’s Seed Fund by NSF, and the Bill & Melinda Gates Foundation. She has served leadership positions in professional communities such as the Industry and Innovation Chair for the International Conference on Artificial Intelligence in Education (AIED). Previously, she hasheld fellowship and positions with Mayor Bloomberg’s Office in New York, the Office of the President at Columbia University, Columbia Technology Ventures, and MTV Networks.
Dr Srećko Joksimović is a Senior Lecturer in Data Science at the Education Futures, University of South Australia. His research is centered around augmenting abilities of individuals to solve complex problems in collaborative settings. As such, Srecko's research agenda is focused on establishing a conceptual framework for understanding the development of the human capacities in sensemaking when augmented with technology. His research investigates the development of a collaborative, technology-literate, AI-ready workforce and broadening the understanding of how different factors (e.g., social, affective, or cognitive) influence the capabilities of groups of workers and learners.
Maria Ofelia Z. San Pedro, PhD is a Senior Research Scientist in the Applied Research group of ACT, Inc. Dr. San Pedro holds a MS in Computer Science from Ateneo de Manila University and a PhD in Cognitive Science in Education from Teachers College, Columbia University, and specializes in learning experiences, assessment and instruction, and analytics within online learning environments. She has over ten years of experience in educational data science research that uses learning technologies and big data (i.e., in education) to drive insights and develop solutions, and three years of experience in software engineering providing usability solutions that support and improve business processes.
Jason D. Way, PhD, is a Senior Research Psychologist in the Center for Social, Emotional, and Academic Learning at ACT, Inc. He holds a PhD in Industrial/Organizational Psychology from the University of South Florida. His research focuses on the assessment and development of the social and emotional skills that lead to success in education and work contexts.
John Whitmer, EdD is an educational data science leader with over a decade of experience conducting research and development using advanced algorithmic approaches. He is currently a Senior Fellow to the Institute for Education Statistics (IES) through the Federation for American Scientists. Prior to this role, John led teams of data scientists, research scientists, and machine learning engineers in large educational assessment companies (ACTNext), edTech providers (Blackboard), and educational institutions (California State University & California Community Colleges).
