Videotutorial ¿Cómo grabar un video para lecciones virtuales basadas en el método Micro Flip Teaching/Aula invertida? Usando ZOOM
Bibliographic record
Abstract
El Programa de Tecnologías Educativas para el Aprendizaje (PROTEA), desarrolló el videotutorial para aplicar el método Micro Flip Teaching, para compartirle a docentes, una estrategia para hacer una lección en casa, mediante vídeo explicativo de una presentación electrónica, utilizando la herramienta Zoom, para grabar la pantalla de su computadora. El método propone aplicar del modelo de aula invertida por medio de tres etapas: lección en casa, actividades adjuntas al material y actividad participativa. El tutorial establece pautas para la creación del material audiovisual, seguidamente propone la preparación técnica previa a la grabación, el procedimiento de grabación en la plataforma Zoom, ejemplificación práctica y finalmente instrucciones para finalización de grabación y gestión de los archivos.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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; both teacher heads agree on what is shown here.
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".