Flipping the Script on Project Management Practices in Education: Outcomes of Applying Agile Development Methodologies in a Classroom Setting
Bibliographic record
Abstract
In this paper, an application of Agile Devel-opment Methodologies (ADM) to university project ori-ented courses is presented. A multidisciplinary student team applies Waterfall and Agile (Scrum) project man-agement strategies over a period of 10 months to a pro-ject based capstone course. The study primarily focuses on evaluating the two methodologies across five catego-ries – student confidence, student awareness, stakeholder confidence, stakeholder awareness, and project success. Results suggest that overall Agile can be more effective than Waterfall. Due to practices such as daily standups and frequent sprint planning, the student team and stake-holders found they were not only able to stay up to date with the progress of the overall project but also found there was enough time allocated to address the ever-changing nature of requirements brought on by the pro-ject. The lessons learned and recommendations provided in this study are generalized such that they can be applied in other project based courses as well.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".