Exploring the Relationship Between Computational Thinking Dimensions and Achievement in Robotics Programming among Computer Education Students

This study investigates the relationship between computational thinking (CT) dimensions and achievement in robotics programming among computer education students in Southeast Nigeria. A correlational research design was adopted, and data were collected from 105 students and analysed using Pearson correlation and multiple regression techniques. The results show that algorithmic thinking exhibits the strongest positive correlation with achievement (r = .596, p < .001), followed by evaluation (r = .449, p < .001), generalization (r = .416, p < .001), and abstraction (r = .374, p < .001). However, decomposition negatively correlated with achievement (r = −.392, p < .001), indicating difficulties in effectively applying decomposition skills to robotics programming tasks. Collectively, the five CT dimensions significantly predict students' achievement, accounting for 74.5% of the variance (r² = .745, F(5,99) = 57.845, p < .001). Regression analysis further identifies algorithmic thinking as the strongest positive predictor (β = 1.116, p < .001), while decomposition (β = −.630, p < .001) and evaluation (β = −.449, p < .001) negatively impact achievement. Additionally, students' self-perceptions of their CT abilities generally align with their performance, except for decomposition, where a negative relationship suggests potential gaps in skill application. These results underscore the critical role of algorithmic thinking in robotics programming success and suggest the need for targeted interventions to address challenges associated with decomposition and evaluation skills. The study offers valuable insights for educators and curriculum developers aiming to enhance computational thinking instruction in robotics education.