Revealing the Hidden Structure of Affective States During Emotion Regulation in Synchronous Online Collaborative Learning

Abstract

This study aims to explore the use of advanced technologies such as artificial intelligence (AI) to reveal learners' emotion regulation. In particular, this study attempts to discover the hidden structure of affective states associated with facial expression during challenges, interactions, and strategies for emotion regulation in the context of synchronous online collaborative learning. The participants consist of 18 higher education students (N=18) who collaboratively worked in groups. The Hidden Markov Model (HMM) results indicated interesting transition patterns of latent state of emotion and provided insights into how learners engage in the emotion regulation process. This study demonstrates a new opportunity for theoretical and methodology advancement in the exploration of AI in researching socially shared regulation in collaborative learning.