Integrating Deep Learning to Enhance Low-Carbon Literacy: Evidence from a Green Bean Porridge Project

Adila Fauzia

Authors

Adila Fauzia Universitas Muhammadiyah Semarang, Indonesia

Keywords:

Deep Learning, Hydrocarbons, Low-Carbon Literacy, Pressure Cooking, project-based learning

Abstract

Hydrocarbon learning has the potential to be integrated with environmental and carbon emission issues, but it is still predominantly conceptual without contextual experience. This study aims to analyze the effect of deep learning-based Project-Based Learning (PjBL) through a project of making a Green Bean Porridge using the pressure cooking (presto) method on students' low-carbon literacy, measure its improvement, and describe student responses. The study used a quantitative approach with a one-group pretest-posttest design on 35 twelfth-grade high school students. The instrument was a Likert scale questionnaire covering cognitive, affective, and psychomotor dimensions, supported by student reflection data. The results showed an increase in the average low carbon literacy score from 90.37 to 97.66 (maximum 112). The paired sample t-test showed a significant difference between the pretest and posttest with an effect size of 1.50 (very large category). Reflection data reinforced quantitative findings through increased understanding of concepts, environmental awareness, and energy-saving behavioral tendencies. It was concluded that deep learning-based PjBL through contextual projects effectively improved students' low-carbon literacy. This study recommends the development of broader contextual project learning in sustainability-oriented chemistry education.

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References

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