Learning Sequencing with Bee-Bot: A Study on Improving Computational Thinking and Motivation for Young Learners in Programming Education

Abstract

This Research-to-Practice full paper presents an exploratory study investigating the impact of using a Bee-Bot educational robot simulator to enhance learning sequencing concepts and student motivation among Macao primary school students. Sequencing in computational thinking (CT) is understanding and applying the logical order of steps in problem-solving processes. We introduced a Bee-Bot computer simulator for children to learn sequencing. Our study adopted a pretest-posttest method involving 35 grade two students. The Computational Thinking Test for Beginners (BCTt) was used to assess CT abilities, and the Instructional Materials Motivation Survey (IMMS) was utilized to measure learning motivation. We found a significant improvement in sequencing ability and more advanced CT concepts (loops and conditions) and a significant correlation between those concepts. Departing from the existing literature, we delved deeper into how Bee-Bot's influence on sequencing extended to more advanced CT concepts. Moreover, considering the ARCS motivation model, this study examined how Bee-Bot affects learning motivation at the primary education level. After the intervention, the findings revealed that the students showed significantly higher learning motivation, meaning that the different learning activities using the Bee-Bot simulator positively influenced various sub-dimensions of the ARCS model: attention, relevance, confidence, and satisfaction. The correlation between the IMMS scores and the BCTt outcomes further suggested that enhanced motivation positively correlated with better CT abilities.