Enhancing Teacher Understanding of Students' Learning Sentiment Through Data-Informed Learning Design and Journal Entries (Poster 26)

• In Event: Friday Poster Session 7:45 am
  In Poster Session: Instructional Technology SIG Posters

Abstract

Remote teaching has underscored the significance of a holistic education approach, emphasizing the cultivation of students' multiple intelligences through effective Social and Emotional Learning (SEL). Comprehending students' SEL not only enhances their learning performance but also informs teachers about providing suitable support in social-communicative, metacognitive, and affective learning aspects. This study investigates the impact of Data-Informed Learning Design (DILD) on online teaching and its influence on students' SEL. The findings evince that DILD serves as an effective design model, empowering teachers to assess students' socio-communicative, metacognitive, and affective learning progress. The study advocates for educators to set high teaching expectations and offer personalized, social, data-driven, and instructional assistance to facilitate students' attainment of optimal social and emotional learning outcomes.

Authors

• Chih-Hsiung Tu, Northern Arizona University

• Cherng-Jyh Yen, Old Dominion University

• Alexandra Miller, Northern Arizona University

• Jennifer M. Werner, Arizona State University

• Nesma Ragab Nasr, Northern Arizona University

• Hoda Harati, Towson University