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Optimizing group formation with a mixed genetic algorithm: an empirical study in active reading using marker data
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DOI：10.1007/s11412-025-09452-9.png)
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Abstract
En 中文
Effective group formation is an indispensable yet challenging aspect of classroom-based collaborative learning. While existing group formation algorithms show promising computational performance in controlled settings, their practical impact on diverse, real-world classrooms remains underexplored. This paper presents a mixed genetic algorithm integrated into a data-driven learning platform designed to accommodate both homogeneous and heterogeneous student characteristics simultaneously. Implemented in a senior high school EFL classroom, the approach leverages active reading marker logs for data-driven grouping. It incorporates a WordCloud tool to enhance educators’ and learners’ understanding of group composition. Empirical results indicate that this system improves vocabulary learning, and the marker-based grouping strategies positively influence group learning dynamics. These findings underscore the algorithm’s practical relevance and highlight the benefits of interpretable, adaptive group formation methods for authentic educational contexts.
Keywords:
Mixed genetic algorithm
Active reading
Collaborative learning
EFL education
Wordcloud visualization
Group awareness
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