Bibliographic record
Abstract
Despite the importance of public speaking skills for English for Academic Purposes (EAP) students’ academic and professional success, few EAP textbooks in- corporate authentic, professional speech models. Thus, many EAP instructors have turned to TED talks for dynamic speech models. Yet a single TED talk may be too long for viewing in class and may limit students’ exposure to various speech styles. In the classroom activity described in this article, students listen to short clips from several TED speeches to learn techniques for making supporting points memorable; they then apply these techniques to their own extemporaneous speeches. This article highlights the critical need for authentic speech models in EAP courses; ful lls the need for authenticity by describing a lesson that utilizes several short, dynamic clips from TED talks to teach students how to use compel- ling support in presentations; and highlights positive student learning outcomes from student presentations and re ections over six semesters of instruction. Malgré l’important rôle que jouent les aptitudes à s’exprimer en public dans la réussite académique et professionnelle des étudiants d’anglais académique (EAP, English for Academic Purposes), peu de manuels d’EAP intègrent des modèles de discours authentiques et professionnels. Plusieurs instructeurs puisent donc dans les présentations TED pour trouver des modèles de parole dynamiques. Toutefois, une seule présentation TED peut durer trop longtemps pour montrer pen- dant un cours d’une part, et elle risque de limiter l’exposition des étudiants aux divers styles de discours d’autre part. Lors de l’activité en classe décrite dans cet article, les étudiants écoutent de courts extraits de plusieurs discours TED pour apprendre les techniques qui rendent mémorables les points de repère d’une présentation; par la suite, ils les appliquent dans leurs propres discours improvisés. Cet article souligne le besoin critique pour de modèles de parole authentiques dans les cours d’EAP; répond au besoin d’authenticité en décrivant une leçon basée sur plusieurs courts extraits dynamiques tirés de présentations TED et employés pour apprendre aux étudiants à développer des idées convaincantes qui appuient leurs présentations; et souligne les résultats d’apprentissage positifs découlant de présentations et de ré exions de la part d’étudiants au long de six semestres d’enseignement.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".