Blended Learning as a Theoretical Framework for the Application of Podcasting
Bibliographic record
Abstract
The use of podcasting has attracted the attention of teachers because it is content-rich and is of wide general interest. Users can listen to podcasts via the Internet or download them on to a portable music player. Some also offer video or animation to make the contents more interesting and easier to understand. Accordingly, podcasts that tend to focus on the use of vocabulary can be very effective in language learning. They help learners hear how to pronounce their words clearly and use them appropriately. In addition to the benefit of its multimodal facilities, the main advantage of podcasting lies in the variety and the control it provides. Many podcasts provide an authentic context including those which are vocationally orientated. In other words, learners can access podcasts linked to their field of study or interest. Therefore, this research project explores how podcasts in an ESL (English for specific purposes) environment can be used with foreign language learners. It then discusses learners’ perspectives of podcasts related to certain subjects. Finally, it proposes a number of suggestions for practical strategies and techniques for teaching English through podcasting.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".