Development of Robot to Improve Learning of Programming Skills among Students

• Published in: Iraqi Journal for Computer Science and Mathematics Vol. 4; no. 3
• Main Authors: Ahmad Sobri Hashim, Mohammad Ateff Md Yusof, Noreen Izza Arshad, Aminu Aminu Muazu
• Format: Journal Article
• Language: English
• Published: College of Education, Al-Iraqia University 05.06.2023
• Subjects: Engineering Education; Fundamentals of Programming; Motivation; Problem-based Learning; Robotic Intervention; Technology Education
• ISSN: 2958-0544; 2788-7421
• DOI: 10.52866/ijcsm.2023.02.03.001

The Fourth Industrial Revolution (IR4.0) has shifted the mindsets of engineering students on the importance of IT skills for current and future engineering related jobs. Nowadays, programming is the most fundamental skill that needs to be learnt by the students using mBot technology. The mBot technology is consider as programmable educational robot designed for beginners to learn basic programming concepts which can be assessed and evaluated via bloom's taxonomy framework. It can be a daunting task to learn programming, especially to new students who do not have any prior experience in coding. Average and low performing students are lacking algorithmic skills, where they could not visualize how the programming concepts work. Therefore, this paper presents the effectiveness of using robot to improve students’ learning of the programming concepts. In designing the learning modules, bloom’s taxonomy model and problem-based learning are adopted using mBot. Moreover, a low-cost and pre-programmed line follower robot has been used to demonstrate the outputs from the programs written in a more interactive manner. For the evaluation, pre-test and post-test of Quasi experimental design have been applied involving 40 students who were identified and categorized as average and low performing groups in the course.  The findings show that a significant improvement has been observed from students’ performance and motivation.  As such, the students’ performance is measured based on two phases of experiments. Whereas the students’ motivation is measured based on four motivation attributes: self-efficacy, active learning strategy, programming learning value and performance goal.