Bibliographic record
Abstract
[SPA] El blended learning permite conjugar la acción formativa presencial con la virtual, siendo una manera de facilitar la transmisión del conocimiento a través de la combinación de las clases tradicionales con la inclusión de las tecnologías de la información y comunicación (TIC). La aplicación de este sistema en el ámbito universitario ofrece la oportunidad de ampliar la difusión de los recursos, contenidos y objetivos de la materia impartida. A la vez que a través de sus herramientas virtuales ofrece la posibilidad de convertirse en un aliado a la hora de que el profesorado pueda seguir de manera eficaz y continúa la evolución del trabajo por parte del alumno. Por ello fue la metodología que se escogió para desarrollar y hacer el seguimiento de la vertiente práctica de la asignatura de “Patrimonio Cultural” del curso 2013/14, dentro del grado de “Geografía y Ordenación del Territorio”, cuyos resultados se exponen en el siguiente trabajo. [ENG] B-learning makes it possible to combine face-to-face academic training with virtual learning, providing an effective way to transmit knowledge through both traditional teaching and the use of Information and Communications Technology (ICT). The implementation of this system in the field of university education is an excellent opportunity to further expand resources, contents and goals of the subject. B-learning also provides very useful virtual tools for teachers to continuously keep track of students’ progress. For the reasons above, B-learning was the chosen method to develop and implement the practical side of the subject “Cultural Heritage” in the academic year 2013/14, which belongs to the degree “Geography and Regional Development”, whose results are presented in this work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".