The Magic of Project Resolution in a Short Period of Time: Design Sprint Applied in Higher Education
Bibliographic record
Abstract
The research conducted in this study was applied to multidisciplinary groups of Higher Education belonging to different degrees using the methodology proposed by the Design Sprint (DS) tool for the achievement of different challenges/objectives in a very short time. The methodology used is an adaptation of the one proposed by the DS, carried out in five non-consecutive stages/sessions focused on students of the first two years of different degrees of the CEU San Pablo University. The students, in general, have valued very positively the collaborative work in small groups, the time management and the administration of work under stress. The abandonment of the different challenges was set at 32%. A high level of commitment has been appreciated among the students to reach the proposed challenges, however, the work of the mentors as guides, becomes essential in the first courses of the different degrees in a general way. The application of the SD methodology provides students with an increase in their performance, in their ability to work in teams and to adapt in the best possible way to the demands of a society that is increasingly demanding new technologies. Students have increased their ability to reflect, transform and innovate in the different objectives/challenges/projects demanded by the new circumstances and social strategies. The support of good mentors, critics and specialists in the different areas to be addressed is necessary to offer students a better learning experience.
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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".