Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data

Abstract

Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment.