Articles

The Development of RBL-STEAM Learning Design to Improve Climate Change Literacy Through the Construction of Energy-Efficient Houses in Sixth Grade of Elementary School Students

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Climate change literacy is essential for preparing future generations to understand environmental challenges and take an active role in mitigation and adaptation efforts. This study aims to enhance students’ climate change literacy through the development of Research-Based Learning (RBL) integrated with the STEAM approach, which combines Science, Technology, Engineering, Arts, and Mathematics. Students were engaged in the construction of energy-efficient house projects that incorporated simple electrical circuits, allowing them to apply scientific concepts in a meaningful context. The study employed a research and development methodology using the 4D model, which consists of the stages Define, Design, Develop, and Disseminate. The developed learning materials included lesson plans, student worksheets (LKPD), and a climate change literacy test. A sequential exploratory mixed-methods design was implemented, beginning with qualitative data collection and analysis, followed by quantitative data collection and analysis. The study was conducted at SDN Banjarwungu 2 as the experimental group and SDN Gempolklutuk as the control group. The findings indicate that the RBL-STEAM learning materials are valid, practical, and effective in improving students’ understanding of the causes, impacts, and potential solutions to climate change. The study concludes that integrating RBL-STEAM with project-based learning activities can significantly enhance climate change literacy in primary education.

The Framework of RBL-STEM Learning Activity: Improving Students’ Climate Change Literacy in Solving the Problem on Forecasting the Nutrition Supply of Hydroponic Plants with GNN

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This paper aims to develop the learning activity framework for the RBL model integrated with the STEM approach, especially in improving the students’ climate change literacy in solving the problem on forecasting the nutritional supply of hydroponic plants using machine learning of GNN technique. It is using qualitative research which involves some bibliography study and analytical study. The findings are presented in a table containing six stages, namely stages 1-6. Each stage explains how students learn to collect data using IoT software, namely Thingspeak to collect some agriculture data, and by using Python under Google Colab platform we implement Graph Neural Networks (GNN) in RBL-STEM learning model. The main findings of this research related to RBL-STEM learning is to develop the learning activity framework in solving the problem of forecasting the nutritional needs of hydroponic plants using Thingspeak and google colab software to improve students’ climate change literacy described in stages 1-6. This research also included the development of a framework in improving the students’ climate change literacy in solving the problem on forecasting the nutritional supply of hydroponic plants using. The implication of the findings of this study is that the learning activity framework is ready to be continued in the process of developing RBL-STEM teaching materials to improve students’ climate change literacy in solving the problem on forecasting the nutritional needs of hydroponic plants machine learning of GNN technique.