THE MODEL OF TEACHING PROGRAMMING TO STUDENTS OF HIGHER EDUCATION BASED ON THE SEMIOTIC APPROACH
This article presents a model for teaching programming to higher education students that leverages a semiotic-based approach, emphasizing the importance of symbols, signs, and their meanings within the programming process. Traditional programming education often focuses on syntax and technical skills, but recent studies (e.g., Peirce, 1931; Eco, 1976) suggest that understanding programming languages as semiotic systems can enhance cognitive engagement and foster a deeper comprehension of code structures. Building on the foundational work of semiotics in education (Saussure, 1983; Nöth, 1990), our framework encourages students to approach programming languages not merely as sets of instructions but as systems of meaning, where each component carries both functional and interpretive significance. To assess the effectiveness of this model, we conducted a comparative study involving two groups taught with traditional methods and two groups taught using a semiotic-based approach. Results indicated that students in the semiotic groups demonstrated improved mastery of programming concepts, with performance improvements of up to 25% compared to their peers taught through traditional methods. These findings align with research on semiotics in STEM education (Chandler, 2007; Hoffmann, 2005), supporting the argument that a semiotic framework can improve student comprehension, engagement, and problem-solving skills. This article contributes to the growing discourse on pedagogical innovation in programming education by proposing a semiotic model that addresses both the syntactic and semantic dimensions of programming languages. By treating programming as a system of signs, this approach bridges the gap between abstract theory and practical application, promoting a holistic understanding of programming. Future work will focus on refining this model and exploring its adaptability across diverse programming curricula.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-11-21
- DOI
- https://doi.org/10.5281/zenodo.14196551
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00