Chih-Hsiung Tu, Ph.D. is a Professor at Northern Arizona University, Flagstaff, AZ, USA and an educational technology researcher and consultant with experience in online learning and teaching, open network learning, and instructional technology training. His research interests are using the lens of socio-cognitive learning, socio-cultural learning to examine distance education, online learning community, network learning environments, and social learning analytics.  He has authored many articles, book chapters, books, multiple honors as keynote speaker, professional development, professional conference presentations, and others.  He has served as an executive board member for ICEM (International Council for Educational Media), and International Division at AECT (Association for Educational Communications and Technology).  Dr. Tu has global experience with international scholars from Turkey, Portugal, Brazil, Hong Kong, Singapore, Venezuela, Taiwan, China, Japan, Niger, and Cyprus etc.

Using Learning Analytics to Support Social & Emotional Learning: A Data-Driven Instructional Design Model

Students and educators had unique susceptibility to burnout already even before the pandemic. Learning is not purely a cognitive process, but also is emotionally loaded and situated within a social context. If COVID-19 rendered us alone and lonely, forging instructional and social community building can combat the pall to accomplish learning physically alone but collaborative, interconnective, and together. The pandemic emphasizes the importance of holistic education with fostering students’ multiple intelligences through effective social and emotional learning (SEL) that signals the need for more dynamic and flexible socio-emotional approaches. Owing to the nature concomitant of relational data, social learning analytics offer analytic powers to enhance SEL. Understanding students’ socio-emotional development helps teachers to provide effective, just-in-time, and personalized support. It is also beneficial to inform teachers to provide more adequate social-communicative, metacognitive, and affective learning via data-driven instructional design models. Despite a vast amount of educational data being collected, the data are rarely organized and provided to galvanize students’ and instructors’ learning and teaching. Being able to comprehend students’ intricate SEL, via Data-Informed Learning Design in conjunction with social learning analysis, would support teachers to advance instructional design for continuous and timely improvements. Several dynamic, flexible, and interconnective activities and instructional designs, derivatives of socially and emotionally situated identity theory with learning analytics, are presented to address the learning and teaching needs in face-to-face, blended, remote, HyFlex, or online learning environments.