The underlying potential of NLP for microcontroller programming education

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Rocha, A (Author) Other; Sousa, L (Author) Other; Alves, M (Author) Other; Armando Jorge Sousa (Author) FEUP

Journal

Title: Computer Applications in Engineering Education

Vol. 32

ISSN: 1061-3773

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-016-RP9

DOI: 10.1002/cae.22778

Abstract (EN): The trend for an increasingly ubiquitous and cyber-physical world has been leveraging the use and importance of microcontrollers (mu C) to unprecedented levels. Therefore, microcontroller programming (mu CP) becomes a paramount skill for electrical and computer engineering students. However, mu CP poses significant challenges for undergraduate students, given the need to master low-level programming languages and several algorithmic strategies that are not usual in generic programming. Moreover, mu CP can be time-consuming and complex even when using high-level languages. This article samples the current state of mu CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of mu CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8-bit mu C and project-based learning. While NLP tools emerge as strong candidates as students' mu C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for mu CP education, building on both hands-on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real-world tasks that the mu C is designed to handle, rather than on low-level coding. We also analyzed the specific impact of chatGTP in the context of a mu CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in mu CP courses.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 28