Aulas Virtuales y la Enseñanza Digital: Buenas Prácticas desde la Experiencia y Capacitación Docente en la FACYT UPNFM CURSPS
Bibliographic record
Abstract
The proper use of virtual learning spaces represents an important element in the educational process, the need for teacher training and updating has increased during the COVID-19 pandemic and universities must face these challenges. In this line, this study analyzes the use of virtual classrooms and good practices in digital teaching in the Faculty of Science and Technology (FACYT) of the Francisco Morazán National Pedagogical University (UPNFM) San Pedro Sula Regional University Center (CURSPS), Honduras, from the experience and teacher training during the COVID-19 pandemic, based on a mixed approach, where the variables studied are: virtual classrooms, digital teaching, and teacher experience and training. The sample contemplated in this work includes 24 teachers and 177 students from the third quarter of 2021 from FACYT, UPNFM, CURSPS, using instruments: Likert-type scale, interview and checklist, it was possible to identify the main internal and external tools that were used in virtual classrooms, describe the strengths and weaknesses in the use of these spaces and, finally, it was possible to explain the importance of experience and teacher training in improving educational quality.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".