Aprendizaje universitario entre el aula presencial y remota, un análisis después de la pandemia - [University learning between the face-to-face and remote classroom, an analysis after the pandemic]
Bibliographic record
Abstract
La crisis sanitaria producto del virus SARS-COV-2 estremeció a la humanidad de múltiples maneras, siendo una de estas la educación universitaria. En México como en muchos otros países, el aula presencial experimentó una abrupta transición al aula remota; cambio que tomó por sorpresa a todos los involucrados, evidenciando múltiples dificultades, carencias y necesidades. A un año del retorno a clases presenciales, se analiza las diferencias, ventajas y desventajas del aprendizaje entre la modalidad presencial y remota, desde la percepción del alumnado de una universidad en México. Se siguió un enfoque cuantitativo de tipo descriptivo, considerando un muestreo no probabilístico y por conveniencia, utilizando como instrumento la encuesta. Los resultados revelan ventajas en ambos modelos, no obstante, los informantes develan que la modalidad presencial ofrece las mejores condiciones para la enseñanza-aprendizaje. Desde una visión prospectiva, se identifica que la diligente estructuración de un modelo hibrido convendría ser tomada en cuenta de cara a mejorar la formación universitaria. Morales-Holguín, Arodi; Jacott-Campoy, José Luis; Moraga-Ríos, Oscar David; Hurtado-Abril, María de La Concepción; Mendoza-Morales, Gabriel
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".