TED Talks as an ICT Tool to Promote Communicative Skills in EFL Students
Bibliographic record
Abstract
In many EFL classrooms in Colombia, it is evident how students struggle trying to use English to communicate; nonetheless, with the revolution of ICTs that has taken place in the last years, there is a variety of tools available to support English learning autonomously with applications, blogs, and online courses; however, many of these tools were not originally designed for teaching but can be adapted for such purpose. TED is a website and a downloadable application where videos are shared in which you can see a wide variety of English speakers born in many parts around the world speaking in a fun and familiar manner with the audience about various topics of interest that besides, come along with cultural content, which extends the range of accents, words, expressions, and ways of referring to the same topic. In this action research, we propose a reflection on the incidence of TED talks on the teaching and learning of English as a foreign language. The instruments used to collect data were interviews, questionnaires, and teacher journals. The use of these videos provided the students with all the communicative elements that allowed them to use English to express their ideas. This offers a glimpse of how useful authentic videos and subtitles are when encouraging students to learn English.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".