A Comparative Model of Teaching Programming Languages Utilizing Cloud Compilers in Remote Labs
The challenges of teaching programming have led to the development of various models, approaches, and tools which attempt to overcome a variety of obstacles. In this article, we explore the application of Noam Chomsky's theory of transformational grammar (TG) to the teaching of programming languages (PLs). In the context of the comparative teaching (CT) of programming languages, we propose a model for exploiting TG's deep structure (DS) and surface structure (SS)., using the technology of Cloud Compilers (CC). We design a theoretical framework for the comparative teaching of programming languages, starting from the deep structure of the computational problem. Through this process, we develop the solution algorithm and transform it to its surface structure, implementing it in various programming languages. The writing, compilation and execution of programs is implemented through the online CC programming environments, within the framework of organized remote labs, where no local installation is required on the user's part and they are free to use. This paper contributes to the scientific debate on issues related to the teaching of computer programming by providing a new model for teaching programming languages, called the Comparative Learning Model (CLM).
Authors
- Christos Douligeris (ORCID: https://orcid.org/0000-0002-6849-6693)
- Dimitriοs Kotsifakos (ORCID: https://orcid.org/0000-0002-6656-260X)
- Sarandis Mitropoulos (ORCID: https://orcid.org/0000-0002-3744-855X)
- Dimitrios Magetos (ORCID: https://orcid.org/0000-0003-0916-4979)
Institutions
- University of Piraeus (GR)
- Hellenic Open University (GR)
- University of West Attica (GR)
Publication Details
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1109/educon62633.2025.11016459
- Primary Topic
- Cloud Computing and Remote Desktop Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Piraeus Research Centre
- European Regional Development Fund