Articles

On Students’ Computational Thinking Skills for Solving SRAC and its Theoretical Framework on Multi-Step Time Series Forecasting on River Erosion using GNN under RBL-STEM Learning Stages

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Computational thinking involves the use of computer science principles to solve complex problems, extending beyond simple programming to various life applications. In today’s educational landscape, the promotion of these skills in the classroom is critical, yet students’ computational thinking skills remain underdeveloped due to inadequate learning models. Key indicators of computational thinking include problem decomposition, algorithmic thinking, pattern recognition, abstraction, and generalization. This study presents RBL-STEM learning activities aimed at enhancing students’ computational thinking through solving the Strong Rainbow Antimagic Coloring or SRAC problem and applying it to multi-step time series forecasting on river erosion using Graph Neural Networks or GNN. The research adopts a qualitative narrative method, beginning with the development of a prototype for multi-step time series forecasting on river erosion using SRAC and GNN, and progressing to the formulation of RBL-STEM learning steps. The results include a comprehensive RBL-STEM learning framework ready for implementation in future research. Learning framework offers student and educator a structured approach to integrating STEM on real life issues. By employing RBL-STEM, students are encouraging to solve river erosion problem systematically based on RBL stages. These finding suggest that the implementation of RBL-STEM with innovative mathematical problems such as SRAC can enhance students’ combinatorial skills, leading to practical solutions for everyday issues through education.

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.

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.

The Development of RBL-STEM Learning Materials to Improve Students’ Computational Thinking Skills in Solving Rainbow Vertex Antimagic Coloring Problems and It’s Application for Batik Motif Design

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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 and their aplication to batik matif design. 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 score obtained on each device is 3.58 for the student assignment plan (RTM), 3.47 for the student worksheet (LKM), and 3.64 for the learning outcomes test (THB). The observation result of the learning implementation score was 3.72 with a percentage of 93%. In addition to being valid and practical, the material also meets the criteria for effectiveness. On average, 95% of students in this trial class are classified as complete students and the response from students is positive. Based on the test results, researchers got 23 students who scored above 60. This means that 82% of students in this class have completed and met one of the effectiveness criteria. Student response questionnaires also give more positive responses than negative responses.

The Development of RBL – STEM Learning Materials to Improve Student’s Conjecturing Thinking Skills in Solving Rainbow Vertex Antimagic Coloring Promblems and it’s Application to Supply Chain Management Using ANN

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One of the educational developments in recent years has to do with STEM. The term STEM (Science, Technology, Engineering, and mathematics) is one approach in the learning process that is quite influential to be used today. STEM-based learning focuses students on solving problems in everyday life by combining the four fields of science: science, technology, engineering, and mathematics. The resulting device has met the validity criterion of 3.25≤ Va<4; the suggestion from validators does not change the device as a whole, but only a tiny part. The validity score obtained in each device is 3.6 for RTM (valid), 3.5 for LKM (practical), and 3.6 for THB (practical). This math learning tool also meets the criteria of practicality, and the practitioner’s advice does not change the device as a whole but only a tiny part. In addition to being valid and practical, the device also meets the criteria of effectiveness. The average student in this trial class is classified as a complete student, and the response from students is positive. Based on the test results, researchers found 16 students with 70% presentations who scored above 60.

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.