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Sustainable environmental education: Some machine learning algorithms in the classification of sustainable environmental attitudes

Author

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  • Benzer, Semra
  • Garabaghi, Farid Hassanbaki
  • Benzer, Recep
  • Güni, Hicret Çimen

Abstract

Since the industrial revolution, human beings has shown a great unconscientiousness about the sustainable environment by polluting the air, water, and soil and rapidly consuming natural resources. Therefore, in the name of sustainable development, sustainable environmental education has become the center of attention of governments and consequently raising individuals with the necessary attitudes, values, understanding and skills in sustainable environment has become an important mission. This study was designed to firstly evaluate the students’ attitude towards a sustainable environment and secondly classify the target students based on their attitudes towards sustainable environment using machine learning methods based on a weighted score system based on a 5-point Likert type. The SVM-SMO classifier demonstrated superior performance compared to MLPNN, RBF Network, and Logistic Regression, especially when the training data was limited.

Suggested Citation

  • Benzer, Semra & Garabaghi, Farid Hassanbaki & Benzer, Recep & Güni, Hicret Çimen, 2025. "Sustainable environmental education: Some machine learning algorithms in the classification of sustainable environmental attitudes," Evaluation and Program Planning, Elsevier, vol. 112(C).
    • Handle: RePEc:eee:epplan:v:112:y:2025:i:c:s0149718925001193
    DOI: 10.1016/j.evalprogplan.2025.102652
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    References listed on IDEAS

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