Bibliographic record
Abstract
As we move towards the third decade of the 21st century, the development of emerging technologies continues to grow alongside innovative practices in digital media environments. This chapter presents a comparative case study of two teams (Team A and Team B) in a professional master's program during a 13-week, project-based course. Based on the role of documentation and the reflective practitioner, team blogs representing learner experiences of Agile practices were analyzed. This case study chapter focused on one blog post of a mid-term release retrospective. The results of this case study are framed around Derby and Larson's (2006) Agile retrospectives framework, including: set the stage, gather data, generating insights, deciding what to do, and closing the retrospective. The case study results suggest the need for public documentation of retrospectives and how this can be challenging with non-disclosure agreements. Also, the authors identify the importance of being a reflective practitioner. Future research on educational and professional practices needs to be explored.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".