The Effect of Content Retelling on Vocabulary Uptake From a <scp>TED</scp> Talk
Bibliographic record
Abstract
This study investigates the potential benefits for incidental vocabulary acquisition of implementing a particular sequence of input–output–input activities. More specifically, learners of English as a foreign language (EFL; n = 32) were asked to watch a TED Talk video, orally sum up its content in English, and then watch the video once more. A comparison group (n = 32) also watched the TED Talk video twice but were not required to sum it up in between. Immediate and delayed posttests showed significantly better word‐meaning recall in the former condition. An analysis of the oral summaries showed that it was especially words that learners attempted to use that stood a good chance of being recalled later. These findings are interpreted with reference to Swain's (1995) output hypothesis, Laufer and Hulstijn's (2001) involvement load hypothesis, and Nation and Webb's (2011) technique feature analysis. What makes the text‐based output task in this experiment fundamentally different from many previous studies that have investigated the merits of text‐based output activities is that it was at no point stipulated for the participants that they should use particular words from the input text. The study also illustrates the potential of TED Talks as a source of authentic audiovisual input in EFL classrooms.
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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".