Application of Collaborative Learning Paradigms within Software Engineering Education: A Systematic Mapping Study
October 28, 2023 Β· Declared Dead Β· π Technical Symposium on Computer Science Education
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rita Garcia, Christoph Treude, Andrew Valentine
arXiv ID
2310.18845
Category
cs.SE: Software Engineering
Citations
4
Venue
Technical Symposium on Computer Science Education
Last Checked
4 months ago
Abstract
Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research to computer-mediated technologies. We discussed the 14 papers in depth, describing their goals and further analysing the CSCL research. Almost half the papers did not achieve the appropriate level of supporting evidence; however, calibrating the instruments presented could strengthen findings and support multiple CL paradigms, especially opportunities to learn at the social and community levels, where research was lacking. Though our results demonstrate limited CL educational theory applied in SE Education, we discuss future work to layer the theory on existing study designs for more effective teaching strategies.
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