Tool Development and Application of Group Emotion Awareness in Shared Regulation:Based on Large Language Model Technology Framework
Increasing research indicates that in CSCL(Computer-Supported Collaborative Learning),group awareness of emo-tions contributes to learners'understanding of their emotional tendencies and the team's collaborative atmosphere.This understanding enables timely regulation of individual and group emotional states,collaborative behaviors,goals,and motivations.Grounded in the theory of shared regulation,this study integrates emotional awareness throughout the CSCL process by designing and developing the group emotional awareness tool,SenGAware,to enhance emotion awareness in CSCL.The tool utilizes the natural language generation and contextual understanding capabilities of Large Language Models(LLMs)to mine and assess emotional information.It provides three modes of perception:emotional tendencies,emotional states,and emotional transitions,generating AI emotion reports for learners and teams,along with emotional Q&A interactions.It can more accurately understand and reflect the emotional state of the group,pro-viding real-time monitoring and dynamic display of emotions.The research focused on pre-service teachers in a master's course titled"Teaching Skills Training and Case Analysis"at a university in Shanghai,demonstrating how the tool embeds shared regulatory col-laborative activities for visualized emotional awareness.It evaluates the tool from multiple dimensions,including shared regulation level,quality of emotional interaction,and overall social network.This study enhances internal emotion awareness in CSCL,improves group collaboration capabilities,and offers new perspectives for researchers or teachers to explore learner emotional changes and group emotional consistency during collaboration.The tool also serves as applications for practice and study cases for LLM-based teaching.
Group Emotion Awareness ToolCSCL Shared RegulationLarge Language ModelSenGAware
国家科技期刊平台
NSTL
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