Empowering university teachers for Education for Sustainable Development: the impact of a massive open online course
Bibliographic record
Abstract
This study seeks to evaluate the efficacy of a massive open online course (MOOC) in promoting Education for Sustainable Development (ESD) competences among university teachers. An exploratory study was conducted using mixed methods under the constructive alignment approach. This involved the creation and delivery of a free MOOC focusing on ESD and offered to educators within Latin American higher education institutions. The constructive alignment approach and the CoDesignS ESD framework, supplemented with theoretical sustainability and ESD knowledge, guided the course. Data from 306 participants were collected and analysed, including pre–post surveys with follow-up assessment and participants’ feedback. The study revealed that university teachers exhibit substantial enthusiasm and motivation to engage with ESD concepts. A MOOC strategy shows promise in strengthening the cultivation of ESD competences among university teachers, especially when combined with hands-on practical components. Despite the need for training educators in ESD and the potential of online platforms to reach diverse audiences, the impact of digital education initiatives for ESD educator training remains largely unexplored. The study is also distinct in its focus on the Latin American context.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".