Do Structured Flowcharts Outperform Pseudocode? Evidence From Eye Movements

Computational thinking is a key universal competence, often taught using methods specific to computer science. One step towards achieving it is learning to analyse and create algorithms. Researchers have long been trying to establish how the form of representation of algorithms (pseudocode versus flowchart) affects its understanding and have reached varying, sometimes conflicting results. This article presents findings that provide objective new data on this topic. In our experiment, we used two different types of algorithmic tasks with three levels of complexity and a group of 114 research participants with varying programming skills. In addition, we used an eye tracking technique that allowed us to collect detailed information about the subjects’ attention distribution during analysis of algorithms. Our results show that subjects took significantly less time to analyse flowcharts (than they did with pseudocode), made much fewer errors, and had higher confidence in the correctness of their solution. Based on eye tracking data, a reduced number of both re-analyses of the algorithm and input data re-referencing was observed for graphically presented tasks. The difference in favour of flowcharts was revealed (with few exceptions) for all levels of algorithm complexity (simple, medium, complex), while regarding the duration of analysis the advantage of flowcharts increased with the growing complexity of algorithms. For complex algorithms, a significant relationship was observed between algorithm presentation and level of programming skills versus the duration of task solving and confidence level. Our study strongly supports the idea of using graphic representation of algorithms both when learning to code and in acquiring computational thinking skills.