Investigating How Situational Emotion and Autonomous Motivation Predict Deep Learning Strategies in an Asynchronous Online Learning Environment (Poster 20)

• In Event: Sunday Poster Session 9:35 am
  In Poster Session: Experiential Education and Community Engagement: Scholarship and Practice Poster Session

Abstract

This study examines the impact of situational emotion and autonomous motivation on the adoption of deep learning strategies in asynchronous online learning. Positive situational emotion and autonomous motivation emerged as significant predictors of deep learning strategy adoption. Autonomous motivation positively correlated with adoption, aligning with previous research. Similarly, positive situational emotion, encompassing enjoyment and hopefulness, promoted deep learning strategy adoption. Notably, negative situational emotion showed no significant association. While valuable insights were gained, limitations, such as reliance on self-reported data, underscore the need for diverse methodologies. Additionally, while autonomous motivation accounted for 11% of variability, other unexplored factors may influence deep learning strategy adoption. Future research should explore secondary predictors like personality traits or cognitive abilities for a comprehensive understanding.

Authors

• Lizeng Huang, The Ohio State University

• Kui Xie, University of Missouri