以分析支援社會與情緒學習：資料驅動設計模式

Students and educators had unique susceptibility to burnout even before the pandemic. Learning is not purely a cognitive process, but also is emotionally loaded and situated within a social context. If coronavirus disease-2019 rendered us alone and lonely, forging instructional and social community building can combat the pall to accomplish active learning which may be 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.

並列關鍵字

learning analytics ； social and emotional learning ； Data-Driven Learning Design

參考文獻

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