Teaching Programming in Township Schools: A Mental Model Approach to Developing Computational Thinking

Computational thinking is essential for problem-solving in the digital age, yet disparities in resources, infrastructure and teacher training create unequal learning experiences in programming education. These challenges are especially evident in township schools, where teachers must foster computational thinking with limited access to digital tools and resources. Addressing misconceptions and cognitive overload is central to this process, as flawed mental models hinder learners’ ability to grasp programming concepts. Given the high failure and dropout rates in introductory programming, there is a pressing need to understand how teachers adapt their instructional practices in these contexts. Grounded in mental model theory, this study investigates how teachers impact learners’ conceptual understanding of programming, despite pedagogical and infrastructural constraints. Using a qualitative case study design, semi-structured interviews were conducted with township school teachers, and thematic analysis identified four key instructional strategies. Teachers employ stepwise algorithmic thinking to support problem decomposition, use real-world examples to bridge abstract concepts, incorporate peer-assisted learning to accommodate varied learning paces and engage in practical activities to address prior knowledge gaps. These findings underscore the role of teachers in overcoming learning barriers and promoting computational thinking in township contexts.