Automated Assessment in Mobile Programming Courses: Leveraging GitHub Classroom and Flutter for Enhanced Student Outcomes
April 05, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pedro Alves, Bruno Pereira Cipriano
arXiv ID
2504.04230
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The growing demand for skilled mobile developers has made mobile programming courses an essential component of computer science curricula. However, these courses face unique challenges due to the complexity of mobile development environments and the graphical, interactive nature of mobile applications. This paper explores the potential of using GitHub Classroom, combined with the Flutter framework, for the automated assessment of mobile programming assignments. By leveraging GitHub Actions for continuous integration and Flutter's robust support for test automation, the proposed approach enables an auto-grading cost-effective solution. We evaluate the feasibility of integrating these tools through an experiment in a Mobile Programming course and present findings from a student survey that assesses their perceptions of the proposed evaluation model. The results are encouraging, showing that the approach is well-received by students.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted