Automated Analysis of Preschoolers' Play and Social Behaviors Using Multi-Object Tracking Technology: An Exploratory Approach
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Recently, increasing interest has emerged in developing automated systems to analyze young children's behaviors in early childhood classrooms. This exploratory study utilized multi-object tracking (MOT), a relatively underused technology in early childhood education research, to unobtrusively capture precise positional data on preschool children's movements and interactions within classroom spaces. Unlike previous MOT-based research typically limited to single observation sessions, this study analyzed repeated MOT data collected over six sessions, enabling longitudinal tracking of children's play behaviors and social interactions. Analyses at the individual level revealed play preferences and wandering patterns, while group-level analyses—including cluster analysis and social network graphs—clearly illustrated collective play trends and peer relationships. Overlapping movement trajectories provided nuanced insights into children's social interactions, complementing conventional proximity measures. However, limitations included exclusive reliance on positional data, which may overlook subtle qualitative behaviors. Practically, this suggests that future studies should integrate complementary data sources (e.g., speech or emotional indicators) to provide more comprehensive insights for educators. Despite these limitations, the findings underscore MOT's potential to deliver objective, actionable insights into children's play and social behaviors, significantly supporting educational practice in early childhood settings.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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