Podcasting in Higher Education: Learning Experiences in Face-to-Face and Blended Modalities
Bibliographic record
Abstract
This article presents the results of an interdisciplinary innovation project entitled Podcasting in Higher Education Teaching and Learning. The project was implemented across different subjects and degree programmes at the University of Vic – Central University of Catalonia during the 2021–2022 academic year, using both face-to-face and blended learning modalities in three faculties. The project’s objectives are twofold: firstly, to integrate podcasting as a teaching and learning tool in university environments, and secondly, to conduct a pilot test of its interdisciplinary application for future use in various subjects and university programmes. The study explores podcasting as a learning tool to enhance communication skills across various scientific disciplines at the university level. The results indicate high participant satisfaction, affirming the effectiveness of podcasting in higher education, driven by factors such as innovation, autonomy, creativity and new educational paradigms. However, challenges in implementation and significant variations across degree programmes are noted. The project also highlights the importance of raising awareness within the university community about the role of communication in the dissemination of scientific knowledge.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".