Educational Innovation with Alternative Credentials as a Driver of the Digital Transformation of the University: A Case Study in Latin America
Bibliographic record
Abstract
The dynamics of change in the work environment are becoming more dizzying, given that adopting new technologies generates new knowledge and jobs. This research analyzed a case study of a Mexican university implementing alternative credentials. The method was instrumental case study research, with exploratory and descriptive categories, applying three instruments: documentary analysis of alternative credential programs, a questionnaire, and interviews with the experts involved in designing and delivering alternative credentials. In this case, the implementation of alternative credentials coincided with the reference frameworks of the European Union and the province of Ontario, Canada. Their frameworks mention the vision and institutional mission of alternative credentialing for the value offered, its definition, operation, award processes, accreditation, and quality. The case provides data for interested higher education institutions, such as why to do it, the strategy to follow, the added value offered, the elements that define it and its design, the assessment process and assignment, the timing of accreditation, and where it is recognized. This research contributes recommendations for defining and managing alternative credentials to serve as a reference for other universities interested in incorporating technology-supported educational innovations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".