The Development of RBL-STEM learning materials to improve Student’s Computational Thinking Skills in Solving REDS problems and It’s application on forecasting horizontal farming using ANN

Abstract

Computational thinking skills involve the process of problem-solving, system design, and understanding human behavior by translating its fundamental concepts into computer science. The indicators of computational thinking include formulation, representation, algorithms, automation, and generalization. To enhance higher-order thinking skills, we implemented RBL-integrated learning with STEM. The developed materials meet the criteria of validity, practicality, and effectiveness. The validity results for each learning tool are as follows: Face-to-Face Plan, 3.6; Student Worksheets, 3.5; and Learning Outcome Test, 3.6. The observation results indicate excellent implementation of the learning process. Approximately 85% or 17 students successfully completed the course, and the average student activity score meets the criteria for active participation. The students also responded positively to the materials and the learning experience. In the pre-test results, 20% of the students were categorized as high-level, 55% as medium-level, and 25% as low-level. However, in the post-test results, the percentage of high-level students increased to 60%, medium-level decreased to 25%, and low-level decreased to 15%. The paired samples T-test showed that the p-value for the pre-test and post-test is 0.000001485, which is less than 0.05. Therefore, it can be concluded that there is a significant difference in the average computational skills test scores of the students.