Instructional Process of Design-Based Learning Integration on Computational Thinking: A Framework for Effective Teaching in Course of Physics Experiment Design

Design-Based Learning and computational thinking (CT) are two key components that support each other specifically in the learning process involving the development of experimental design. Both components emphasize the importance of integration between computational thinking and design-based learning to support a creative, innovative and data mining-based learning environment. In this context. The formulation of a holistic instructional process framework by integrating design-based learning and computational thinking is very important. Therefore, this study aims to designing an Instructional Activity Framework of Design-Based Learning Integration on CT. In addition, this study involves design validation and statistical analysis to determine the validity of the design and its effect on the variables of student engagement, computational perspective, and CT process. The instructional process framework was developed using a systematic literature review, validation analysis using the content validity index (CVI), and statistical analysis using a one-sample t-test. This study was conducted in the physics experiment design course with 23 students. The instrument consists of a CVI, student engagement, CT process, and perspective. The analysis showed that the instructional activities of design-based learning integration in CT stages are as follows: (1) Find, define, and develop an idea (abstraction); (2) background research (decomposition); (3) build an artifact (algorithm thinking); and (4) design the final product (generalization and evaluation). An I-CVI/Ave score of 1 means the design check using the CVI was acceptable. Moreover, the result of one sample t-test analysis showed that the implementation of the learning process framework was significantly influenced by student engagement (p < 0.05), CT process (p < 0.05), and CT perspective (p < 0.05). Therefore, these results support the learning process framework, specifically in the physics experiment design course.