Strengthening Policies for Education, Innovation, and Digitization Through Teacher Training: Evaluating ProFuturo’s Open Model in Ecuador
Bibliographic record
Abstract
Teacher training and a commitment to innovation in teaching are determining factors in the success of technology adoption processes. This article presents a study on the opportunities produced through the collaboration of the Ecuadorian Ministry of Education and the ProFuturo program, which arose during the COVID-19 pandemic. This collaboration resulted in the improvement of digital competency among teachers and pupils and in transference to educational practise. It also strengthened the existing limited capabilities for developing mass training programs for teachers in the country. The research was conducted through an online survey, with a cross-sectional, quantitative, and non-experimental focus from two data sources. A total of 3,565 teachers answered the digital survey for teachers trained using the Open Model in Ecuador between 2020 and 2022. On the other hand, 7,257 teachers answered the ProFuturo Self-Assessment of Digital Skills of Teachers (https://competencyassessment.profuturo.education/?lang=en). The results show an improvement in the competency of teachers following their participation in the program and confirm that they considered digital transformation in the classroom to be of great utility. Teacher training remains a cornerstone of high-quality education and research as this contribution proves a positive impact on learning experiences, where there was a significant transference, driven by an improvement in digital skills applied to the teaching process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".