Pedagogical Support in, A Hybrid Setting, in Public Institutions and PRONOEI of the Initial Level
Bibliographic record
Abstract
Pedagogical accompaniment is a strategy that seeks to improve teaching practice through a reflection process to analyze and improve performance in the classroom, in order to improve student learning outcomes.The objective of this research is to be able to explain how the pedagogical accompaniment from a hybrid scenario favors the improvement of learning in educational institutions and PRONOEI of the initial level, appropriating the contributions that technology has left us in recent years and use them to generate commitments to improve learning.In order to analyze the information, a meta-analysis was used to summarize and combine the results of the research, as well as information from databases such as Scopus, Scielo, Dialnet, Redalyc, ProQuest, Springer, Redib.Web of Science among others.others; and a total of 4 doctoral theses, 5 technical standards, 4 pedagogical manuals and 42 scientific articles, of which 16 are presented in English, 2 in Portuguese and 24 in Spanish, the years of publication range between 2010 and 2022 and the countries are diverse, among which we can mention Spain, Chile, Colombia, Cuba, Mexico, Peru, Ecuador, France, Russia, Venezuela, Ukraine, Brazil, South Africa, Argentina, the Dominican Republic, the Philippines, Italy and Canada.The search strategies were by topic and keyword.The information collected has shown that pedagogical accompaniment is important to improve learning and this must be promoted through relevant and sustainable public policies.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".