Optimizing Strategies for Geospatial Perception Development in Early Childhood Science Education Based on Fuzzy Clustering Algorithm

Authors

  • Debin Si Normal College, Jinhua University of Vocational Technology, Jinhua 321000, Zhejiang, China

DOI:

https://doi.org/10.70917/ijcisim-2026-0099

Keywords:

fuzzy clustering; fuzzy C-means algorithm; early childhood education; geospatial perception abilities; ability cultivation strategies

Abstract

Spatial perception ability is a core component of young children's scientific literacy, involving the coordinated development of multidimensional abilities such as spatial orientation. This study proposes an optimized method for cultivating young children's geospatial perception abilities based on fuzzy clustering algorithms (FCM and FLICM). Combining Piaget's three-stage theory of cognitive development (topological → projective → plane geometry stage), targeted cultivation strategies were designed and group experiments were conducted. The experimental group received strategy intervention, while the control group received traditional teaching. With 217 young children as the research subjects, five ability assessments were conducted: spatial orientation, map cognition, proportional perception, spatial relationship reasoning, and directional description. Fuzzy clustering algorithms were applied to identify six typical ability groups. These included the comprehensive development type (Cluster 1 = 91.48 ± 4.44), map cognition impairment type (Cluster 4 = 74.15 ± 6.13), Comprehensive Delay Type (Cluster 6 = 47.59 ± 7.08), etc. The marginal effect values for MC3: 3D-2D Conversion (17.86%) and SR2: Spatial Pattern Recognition (18.37%) were identified as core weaknesses. After a 30-day teaching experiment, the post-test results showed that the experimental group's total score significantly improved, with a total score of 90.47 ± 9.32, while the control group scored 80.70 ± 10.97, t = 12.438, p = 0.000. The proportion of high-scoring segments (≥85 points) reached 80.6% (87 people), an increase of 43% compared to the control group's 37.6%. The number of children in the low-score segment (≤70 points) decreased sharply to 4 (26 in the control group). This validated that ability diagnosis and strategy optimization based on fuzzy clustering can effectively enhance young children's geospatial perception abilities, particularly for high-scoring groups and key ability shortcomings (MC3, SR2).

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Published

2026-01-25

How to Cite

Debin Si. (2026). Optimizing Strategies for Geospatial Perception Development in Early Childhood Science Education Based on Fuzzy Clustering Algorithm. International Journal of Computer Information Systems and Industrial Management Applications, 18, 13. https://doi.org/10.70917/ijcisim-2026-0099

Issue

Section

Original Articles