Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms - Fatma Merve Mustafaoğlu; Fatma Alkan (Science Education International)
Recycling waste is essential to mitigate environmental damage caused by human activity. Environmentally responsible behaviors, shaped during early ages, are closely linked to environmental attitudes, as demonstrated by prior research. This study aims to predict middle school students’ recycling behaviors using machine learning algorithms. A correlational survey model was employed, involving 574 middle school students in Turkey. Data were collected using the Environmental Attitude Scale, Recycling Knowledge Test, and Plastics Recycling Information Test. Logistic regression analysis was conducted to determine relationships among environmentalbehavior, environmental emotion, plastics recycling knowledge, and recycling behavior. Results revealed that recycling behavior is positively and significantly predicted by plastics recycling information, environmental behavior, and negatively significant relationship with environmental emotion. These variables emerged as strong and reliable predictors of students’ recycling behaviors. This study highlights the importance of fostering environmental knowledge and emotional engagement to encourage responsible recycling practices among young learners.