Competency-Based Approach in the Initial Training of Secondary Education Teachers: A Case Study
Bibliographic record
Abstract
Initial teacher training for secondary education in Spain has evolved through various legislative reforms, ultimately leading to the establishment of the Master's degree in Secondary Education Teaching (MPES). However, this model has been criticized for its lack of alignment between theory and practice, insufficient pedagogical training, and uneven implementation across different autonomous communities. This study aims to analyze the competency-based model of teacher training within the MPES at the University of Valencia and Florida Universitària (a center affiliated with the aforementioned institution). The study employs a qualitative methodology, involving a documentary analysis of a specific case study. Based on the selected data analysis tools, the study reviews a range of competencies promoted in the teaching guides and verification reports, contrasting them with current regulations regarding competency-based training needs in the MPES. The results reveal a disconnect between the competencies specified in the regulations and their presence in academic programs, with an emphasis on the disciplinary knowledge of teacher training and inclusive education. Thus, the study concludes that it is necessary to revise and update initial teacher training to ensure its consistency and adaptation to the current challenges of the education system.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".