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Empirical Software Engineering

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01-11-2023

We do not understand what it says – studying student perceptions of software modelling

Authors: Shalini Chakraborty, Grischa Liebel

Published in: Empirical Software Engineering | Issue 6/2023

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Abstract

Background

Despite the potential benefits of software modelling, developers have shown a considerable reluctance towards its application. There is substantial existing research studying industrial use and technical challenges of modelling. However, there is a lack of detailed empirical work investigating how students perceive modelling.

Aim

We investigate the perceptions of students towards modelling in a university environment.

Method

We conducted a multiple case study with 5 cases (5 courses from 3 universities) and two units of analysis (student and instructor). We collected data through 21 semi-structured interviews, which we analysed using in-vivo coding and thematic analysis.

Results

Students see some benefits of modelling, e.g., using models for planning and communicating within the group. However, several factors negatively influence their understanding of modelling, e.g., assignments with unclear expectations, irregular and insufficient feedback on their models, and lack of experience with the problem domains.

Conclusions

Our findings help in understanding better why students struggle with software modelling, and might be reluctant to adopt it later on. Our recommendations on modelling education could help to improve education and training in software modelling, both at university and in industry. Specifically, we recommend that educators try to provide feedback beyond syntactical issues, and to consider using problem domains that students are knowledgeable about.

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Title: We do not understand what it says – studying student perceptions of software modelling
Authors: Shalini Chakraborty
Grischa Liebel
Publication date: 01-11-2023
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 6/2023
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-023-10404-w

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Abstract

Background

Aim

Method

Results

Conclusions

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