Articles

The Development of RBL-STEM Learning Materials to Improve Students’ Information Literacy in Solving Rainbow Antimagic Coloring Problem for ETLE Technology

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Students often struggle to solve complex mathematical problems in real-world contexts due to low information literacy skills. To improve information literacy skills, an effective learning approach can be RBL-STEM, which provides research-based learning and can be practically applied in the real world. This study aims to investigate RBL-STEM activities, describe the process and results of developing RBL-STEM materials, and analyze data from the results of developing these materials to improve students’ information literacy skills. The method of research used is research and development (R&D). The purpose of this research is to develop RBL-STEM materials and produce learning material products in the form of semester learning plans, student assignment designs, student worksheets, and learning outcomes tests. The results of the materials development showed validity with a validity criterion of 92.75%. The trial was conducted with 40 students, and the implementation using the RBL-STEM materials was found to be practical with a practical criterion of 98.75% and effective with an effectiveness criterion of 94%. In addition, the students were highly engaged and responded very positively to the learning experience. Pretest and posttest analysis showed an increase in students’ information literacy in solving the rainbow antimagic coloring problem. The study also identified three levels of information literacy skills: high, medium, and low. Statistical analysis, phase portrait, NVivo, and word cloud confirmed the research findings and showed an increase in students’ information literacy skills. Thus, RBL-STEM has the potential to improve students’ information literacy in real-world contexts, such as the application of ETLE using graph neural network techniques.

Development of RBL-STEM Learning Tools to Improve Students’ Computational Thinking Skills Solving Rainbow Antimagic Coloring Problems and Their Application to Traffic Flow Problems with Spatial Temporal Graph Neural Network

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Computational thinking is thinking process that is needed in formulating problems and solutions, so that these solutions can be effective information processing agents in solving problems. Indicators of computational thinking consist of problem decomposition, algorithmic thinking, pattern recognition, abstraction and generalization. To improve higher-order thinking skills, we apply RBL learning integrated with STEM approach. To improve students’ thinking skills, it is necessary to develop tools that support the success of learning activities. The learning tools that have been developed meet the criteria of valid, practical, and effective. The validity scores obtained by each device are 3.5 for the face-to-face plan, 3.41 for the student worksheet, and 3.56 for the learning outcomes test. The observation result of the learning implementation score was 3.8 with a percentage of 95%. There were 23 students who completed or around 88,46%, percentage of average score of student activities was 94.17%, and as many as 94.47% of students gave a positive response.