Scrum Methodology in Higher Education: Innovation in Teaching, Learning and Assessment
Bibliographic record
Abstract
The present paper aims to detail the experience developed in a classroom of English Studies from the Spanish University of Málaga, where an alternative project-based learning methodology has been implemented. Such methodology is inspired by scrum sessions widely extended in technological companies where staff members work in teams and are assigned tasks within long-termed projects. Students were initially reluctant and afraid to work in teams but, as the experience advanced, their point of view was changing. Thus they positively stated that this methodology encouraged themselves to participate and to change ideas, with a deeper feeling of empathy, self-organisation and self-knowledge. At the end, most of the students declared they would participate again in a similar activity. Hence, considering the opinions from the students (and also from the teachers), and after observing the whole experience and analyzing the documents generated in an electronic portfolio, we think this method can be considered as a good proposal to accomplish a teaching-learning process of high quality at universities for three main reasons: first, it improves the capacity of using the knowledge in a disciplined, critical and creative way; second, it promotes the coexistence in heterogeneous human groups; and third, it develops the capacity of thinking, living and acting with complete autonomy.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".