A low-level approach to improve programming learning

Autor: Carlos Villagrá-Arnedo, Faraón Llorens-Largo, Rafael Molina-Carmona, Rosana Satorre-Cuerda, Patricia Compañ-Rosique, Francisco J. Gallego-Durán
Přispěvatelé: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial, Grupo de Investigación en Tecnologías Inteligentes para el Aprendizaje (Smart Learning)
Rok vydání: 2020
Předmět:
Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
ISSN: 1615-5297
1615-5289
DOI: 10.1007/s10209-020-00775-y
Popis: Learning to program is becoming a universally desired ability. Discovering better ways to teach programming and improving existing ones is essential to increase its accessibility. At present, most teaching approaches focus on high-level languages and constructs to ease understanding. However, understanding problems seem to persist making the learning process slow and painful. Moreover, mental models developed by students present gaps and misunderstandings that limit their maximum achievable abilities. This paper presents a new approach to teach students bottom-up, starting from machine code and assembler programming. This approach has been tested on first-year university students for two consecutive years. Experimental groups attended a 16 h course the week before their first term at the university. Then, their performance was comparatively measured against the control group through their marks on the introductory Programming 1 subject. Several potential confounding factors were also considered. Results suggested that such a small intervention could have positive, though limited, influence in their programming abilities. The experimental setup is detailed, and all data gathered are included for reproducibility. This research is partially supported by Cátedra Santander Universidad de Alicante de Transformación Digital.
Databáze: OpenAIRE
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