On the Nature of Collaborations in Agile Software Engineering Course Projects
Bibliographic record
Abstract
If there is a constant in software development, then it is that its ecosystem is a variable. The agile methodologies are part of a relatively recent shift from predictive to adaptive approach towards software development. This change has had a notable impact on software engineering education (SEE). In this article, a glimpse into the state-of-the-art of incorporating agile methodologies in software engineering courses is presented. In doing so, the reasons for including a project component in software engineering courses, and for committing to agile methodologies in software engineering projects in those courses, are given. The significance of collaboration in the execution of agile methodologies, in general, is underscored, and the pivotal role of collaboration in agile course projects, in particular, is emphasized. To lend an understanding to the notion of collaboration in agile methodologies, a conceptual model for collaboration is proposed and elaborated. The types of collaborations that can occur in agile course projects are classified and discussed. The use technological means for facilitating collaboration, including the Social Web and especially the Wiki, is highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".