Exploring developmental trajectories of analogical reasoning in preschool children: A cognitive diagnostic approach enhanced by IoT technology

This study examines the developmental characteristics of analogical reasoning in preschool children, utilizing Cognitive Diagnostic Models (CDMs) and innovative Internet of Things (IoT) technology to enhance assessment accuracy and efficiency. Participants included 539 preschool children (mean age = 66.87 months, SD = 7.24) from four kindergartens across two cities in eastern China, with 284 boys and 255 girls, representing middle and senior class levels. The analogical reasoning test was designed using animal figure tasks, calibrated with Optical Identify (OID) technology for automated data collection. Results indicate significant age and gender differences in analogical reasoning ability and cognitive attribute mastery patterns. Senior-class children demonstrated higher levels of analogical reasoning, while girls generally outperformed boys. The findings suggest that OID-enhanced cognitive diagnostic assessments effectively capture real-world interactions, supporting detailed diagnostic reports on preschoolers' cognitive strengths and weaknesses. This study contributes to early childhood education by offering a scalable, technology-based assessment model that may improve individualized educational support for cognitive development.