In early childhood science education, analyzing and responding to children’s preconceptions are essential professional skills possessed by preschool teachers. This study aims to evaluate the level of preschool teachers’ skills of analyzing and responding to the development trajectories of children’s preconceptions (DTCP) and explores the relationship between them in different science disciplines as well as between teachers with different teaching experiences from a Chinese teachers perspective. A newly developed and validated instrument, the Situational Judgement Tests of Preschool Teachers’ Skills to Analyze and Respond (SJTs-PTSAR), is adopted. Altogether, 1084 Chinese teachers from three cities in China were surveyed, and analysis of the psychometric properties indicated that SJTs-PTSAR was a reliable and valid scale. The means and standard deviations of preschool teachers’ analysis skills were 1.04 and 0.31, and those for responding were 1.02 and 0.26. There was no significant difference between the scores of the two skills (t=−1.842,p>0.01, Cohen’s d = 0.068). Correlation analysis showed that the preschool teachers’ analysis skills were positively related to their responding (r=0.353,p<0.001), and there was a significant correlation between the skills of teachers of different teaching ages. These results showed that preschool teachers’ skills to analyze and respond to the DTCP were at a medium level, and an accurate analysis could not guarantee a high-level response based on the DTCP. The correlation coefficient between these two skills with teachers of different teaching experience was nonlinear. A number of suggestions for teacher training and professional development are provided to promote the sustainable development of teachers’ analysis and response skills.

Emerging information technology such as Internet of Things (IoT) has been continuously applied and deepened in the field of education, and the learning analytics technology based on children’s games is gradually moving toward practical application research, but there are few empirical studies on the micro level of emerging information technology and learning analytics methods in the evaluation of young children’s learning process and learning effects. As the research content, the study examines preschool children’s analogical reasoning abilities, reflecting their thinking levels and processing abilities. Using a decision tree model in learning analytics, the process data and result data of children’s analogical reasoning games based on Internet of Things technology are analyzed, and the classification model of preschool children’s analogical reasoning is constructed. The study found that the learning analysis of analogical reasoning based on games mediated by IoT technology is feasible and effective.