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Investigating Optimal Learning Moment to Enhance Student Engagement and Social Emotional Leaning
Sun, April 24, 8:00 to 9:30am PDT (8:00 to 9:30am PDT), Marriott Marquis San Diego Marina, Floor: North Building, Lobby Level, Marriott Grand Ballroom 2Session Type: Symposium
Abstract
Optimal learning moment (OLM) refers to a productive moment for student leaning (Shernoff & Csikszentmihalyi, 2009; Schneider et al., 2016). This symposium is an international symposium and includes several new approaches to analyze OLM for high school students across different countries. The results also present insights into how the relationships can be similar and different across different country contexts. Paper one examines students’ situational socio-emotional skills and OLM. The second paper examines the connection between feeling challenge, stress, anxiety, and determination. The third paper presents how OLM occurs, fluctuates, and stabilizes. The fourth paper focuses on the co-occurrence of emotional states and OLM. The fifth paper demonstrates how to assess the reliability and validity of ESM data over the years.
Sub Unit
Chair
Papers
Situational Socio-Emotional Skills Among Finnish High School Students - Katja Upadyaya, University of Helsinki; Katariina Salmela-Aro, University of Helsinki
Intersections of Challenge, Stress, and Anxiety: When Is the Challenge Too Much? - Lydia Bradford, Michigan State University; Kayla Bartz, Michigan State University
Exploring the Stability and Fluctuation of Experiencing "Challenges" in the High School Science Classroom - I-Chien Chen, Michigan State University; Lydia Bradford, Michigan State University; Kayla Bartz, Michigan State University; Joseph S. Krajcik, Michigan State University
Optimal Learning Moments in Finland and U.S. Science Class: Co-Occurrence Network Analysis - Xin Tang, Shanghai Jiao Tong University; I-Chien Chen, Michigan State University; Jari Lavonen, University of Helsinki; Joseph S. Krajcik, Michigan State University; Katariina Salmela-Aro, University of Helsinki
Situational and Individual Validity and Reliability of the Experience Sampling Method: Intensive Longitudinal Data - Christopher R. Klager, Northwestern University; I-Chien Chen, Michigan State University