Abstract
Learning to program is a complex task that involves multiple aspects. One of the skills developed with programming expertise is coding style. Cultivating good programming habits not only improves code quality but also fosters a deeper understanding of the language. In recent years, increasing attention has been given to code quality and defects from the very first programming course. This trend can be integrated into the training of pre-university teachers, which is essential for introducing computer science into school curricula. This paper presents a catalog of code defects for the block-based language ScratchJr. These defects can be shown to pre-service teachers to raise awareness of potential issues in the programs they create. The proposed defects are grouped into four categories: dead code, unpredictable behavior, ineffective code, and poor programming style. They were identified through a combination of the authors’ experience with the language and the analysis of projects developed by trainee teachers. The paper concludes with suggestions for future work.
Sergio Cavero
Phd in Artificial Intelligence
Sergio Cavero was born Madrid (Spain) on September 24, 1997. He graduated in Software Engineering from Universidad Politécnica de Madrid in 2019. During his undergraduate studies he made a stay at the University of Bradford (UK). In addition, he was awarded twice with the ‘Beca de Excelencia of the Comunidad de Madrid, and also awarded for the Best Final Degree Project. Later, he completed a Master’s Degree in Artificial Intelligence at the same university (UPM) obtaining awards for Best Academic Record (‘Premio José Cuena’) and Best Master’s Thesis. He academic results lend him be beneficiary of one of the ‘Ayudas Para la Formación de Profesorado Universitario (FPU)’, funded by the Spanish Government. He is currently carrying out his doctoral thesis at the Universidad Rey Juan Carlos, supervised by Professors Abraham Duarte and Eduardo G. Pardo. His main research interests focus on the interface among Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristics procedures for optimization problems modeled by graphs.