University Teachers and Open Educational Resources: Case Studies from Latin America
Bibliographic record
Abstract
The Open Education movement has made efforts to systematise experiences and to evaluate the adoption of Open Educational Resources (OER). However, OER adoption is not part of the prevailing paradigm in higher education, both at the global level and in Latin America. This paper describes results of a study that analysed the social representations regarding the development, use, and reuse of OER by university teachers in their pedagogical practices. We conducted a study of 12 cases from Latin American universities using data analysis based on Grounded Theory. The results show that the use and reuse of OER lacks of public and institutional policies. The main agents are teachers organised in teams that support OER adoption. The reasons that encourage the creation of OER are mainly intrinsic, such as the pleasure derived from contributing and sharing, as well as external and related to professional development needs from the reflection on one’s own educational practice. Educators consider it essential to evaluate the resources created so that they can be reused in continuous improvement processes. Commercial use and misappropriation of the works are two of the main tensions identified. The community factor of teaching guides most behaviours in OER adoption in educational institutions and is presented as an inherent part of the development and transformation of the curriculum.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".