Getting Closer to Authenticity in the Course of Technical English: Task-Based Instruction and TED Talks
Bibliographic record
Abstract
Authentic materials, if appropriate to the learning situation, might turn the classroom environment into a more engaging place, where motivation might be generated through the performance of meaningful tasks. This article describes how a Text-Based Instruction approach can provide the basis for the design of an ESP syllabus based on relevant, varied and engaging tasks to enhance authentic language use among engineering undergraduates. The design of these tasks mainly draws on TED Talks that are specifically technological and connected to engineering undergraduates, as the talks develop novel and thought-provoking ideas which are interesting and personally meaningful and relate to different engineering fields. These tasks are specially designed to enable students to carry out a process of talk deconstruction through the analysis of distinct discourse and linguistic features specific to the spoken genre of TED Talks. This analysis ultimately aims at the eventual construction of students’ oral presentations. Oral presentations can be conceived as an activity that approximates the real world and future workplace of engineering undergraduates, and in consequence, promotes students’ instrumental motivation.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".