رده‌بندی جانوران با رویکرد عددی: مطالعه موردی از گوناگونی جانداران در کتاب علوم تجربی پایه نهم

The aim of this study is to propose a model for the classification of organisms in biology education. The research method is analytical and inferential, employing morphological data to determine relationships and conduct numerical taxonomy, calculate the Manhattan distance matrix among species, and apply the neighbor-joining method to manually construct a phenogram through direct calculations without the use of software.The statistical population consists of the content of Chapter Eleven of the ninth-grade lower secondary science textbook, entitled “Diversity of Living Organisms,” and the morphological data were extracted through analysis of the samples presented in the textbook images.

The findings of this study show that numerical methods based on mathematical and statistical algorithms can provide a clear pattern of phenotypic relationships among the invertebrates examined, demonstrating that morphological data are capable of reflecting the general structure of evolutionary relationships. The resulting phenogram indicates that numerical classification techniques can serve as valuable tools for analyzing relationships among animal groups, especially when molecular data are not available. However, to achieve a more comprehensive understanding of evolutionary patterns, integrating these methods with genetic, behavioral, and ecological data would offer a more effective and holistic strategy. In general, phenotypic analyses using Manhattan distance and hierarchical clustering methods can reconstruct large-scale evolutionary structures with reasonable accuracy. Nevertheless, it must be emphasized that morphological data do not always capture all aspects of evolution and may be influenced by convergent evolution or the reduction or elaboration of traits.

The use of numerical approaches in animal classification has led to significant advancements in understanding evolutionary relationships and scientific taxonomy. By analyzing molecular, morphological, and other biological characteristics, these methods produce more accurate phylogenetic trees and provide objective, quantitative foundations for animal classification.

Based on this study, incorporating topics on organismal diversity—particularly with an emphasis on native species—into school textbooks and drawing students’ attention to ecological importance, interactions among living organisms, and the identification of independent evolutionary units can contribute to effective conservation planning. Ultimately, the findings highlight that presenting scientifically grounded inferential models is essential for enhancing the understanding of organismal divergence and for improving the scientific competencies of biology teachers, thereby supporting the education of a scientifically informed generation capable of engaging with and contributing to a technology-driven world.