GOOD PRACTICES FOR VIRTUAL CLASSROOM IN UNIVERSITARY BLENDED LEARNING BUENAS PRÁCTICAS DE AULAS VIRTUALES EN LA DOCENCIA UNIVERSITARIA SEMIPRESENCIAL
Bibliographic record
Abstract
<!-- @page { margin: 0.79in } P { margin-bottom: 0.08in } --> We present the design and results of a study conducted at the University of La Laguna (ULL) to identify best practices in virtual classrooms in Higher Education developed in the form of blended learning. The study was conducted in the first quarter of 2008 and analyzed a large sample of virtual classrooms (N = 107) in the Virtual Campus of the University during the period 2005-07. This article presents six examples of virtual classrooms by scientific fields characterized by the incorporation of information resources, communication and experiential learning. <!-- @page { margin: 0.79in } P { margin-bottom: 0.08in } --> En este artículo presentamos el diseño y resultados de un estudio realizado en la Universidad de La Laguna (ULL) destinado a identificar buenas prácticas de aulas virtuales en la docencia universitaria desarrolladas bajo la modalidad de blended learning o enseñanza semipresencial. El estudio se desarrolló en el primer trimestre del año 2008 y analizó una importante muestra de las aulas virtuales (N= 107) existentes en el Campus Virtual de dicha universidad en el periodo 2005-07. Se seleccionaron seis ejemplos de aulas virtuales, clasificados por campos científicos, caracterizadas por la incorporación de recursos de información, de comunicación y de aprendizaje experiencial.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".