La autorregulación y la autoevaluación como parte de la estrategia de aprendizaje en los cursos obligatorios que ofrece el centro de capacitación en educación a distancia
Bibliographic record
Abstract
Three years ago, the Distance Education Training Center (CECED) began to implement actions to improve self-regulation and self-assessment strategies on its courses and following the student centered orientation indicated in UNED´s Teaching Model.\n\nIn that sense, this paper aims to describe the systematization of the experience of implementing self-regulation and self-assessment strategies on two courses offered in the first quarter of 2014. The courses Pedagogy for Distance Education and Organization and Design of Online Courses have been modified in terms of methodology, to promote learning processes by using self-regulation and self-assessment procedures. \n\nAlso, course participants are encouraged to implement these strategies in the courses they teach. These strategies and the methodological adjustments will be evident in different course components of the training sessions not only in the self-assessment instrument.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".