Research on Autonomous Outdoor Game Instruction Strategy Based on K-Means Clustering Algorithm

The outdoor games of young children are mainly in autonomous way, which can intuitively reflect the inner thoughts of young children and promote the all-round development of young children, in which the teachers have an important guiding role in the games of young children. Therefore, the study proposes the analysis of autonomous outdoor play guidance strategies based on K-means clustering algorithm, through questionnaire survey and interview observation method to investigate the autonomous outdoor play guidance strategies of 615 early childhood teachers with different characteristics and classify the teachers’ guidance strategies into three types by combining the K-means clustering algorithm. Statistical results found that there were four dimensions of significant differences among the three types of teacher instructional strategies, with the autonomous exploratory type having the highest overall scores, indicating that autonomous outdoor play has a positive effect on the development of young children's physical health and behavioral interactions. The study proposes appropriate teaching guidance for the results analyzed in this survey, giving full play to the role of role models and sharing relevant experiences to learn from others’ excellent experiences and supplement their own teaching shortcomings.