ABSTRACT
Problem-solving skills are part of Critical Thinking which focuses on the student's abilities to face the 21st century. Recently, one technique to foster and improve problem-solving skills is by learning through Computational Thinking. Many previous studies have described that Computational Thinking is a problem-solving technique adopted from Computer Sciences. However, Computational Thinking learning had been carried out in class or asynchronous. The COVID-19 situation raises the challenges of how Computational Thinking can be taught online. Therefore, this study aims to develop an instructional design in online-based computational thinking learning. The development of this study employs the ADDIE method. The final step of the development stage was carried out through a small-scale online class experiment. The development result obtained by Scratch, which is an online programming platform that is suitable for 1) learning Computational Thinking, 2) instructional design that refers to problem-based learning, and 3) instructional strategies that refer to Bloom's taxonomy. The results of the small-scale instructional design experiment showed that there was a significant improvement in the students' ability in problem-solving and computational thinking. © 2023 Author(s).
ABSTRACT
The development of image processing science is needed to solve problems that are often faced by humans, especially in the field of computer vision. One application of the image processing system is on a package delivery mission during the Covid-19 pandemic. Drones are used to send packages by detecting the presence of Qr Code to determine the point of delivery location. In this study, tests will be carried out on the maximum distance (vertical and horizontal) that can be detected by the Qr Code detection system and the length of time to detect the presence of the Qr Code (time spent). The test shows that the greater the data collection distance (vertical and horizontal), the longer the system detects the presence of the Qr Code. The maximum horizontal distance that the Qr Code can detect is 155 cm, while the vertical distance is 115 cm. The detection distance at vertical is smaller than horizontal because the vertical distance is affected by the field of view (FoV). © 2021 Published under licence by IOP Publishing Ltd.