Teaching Formulaic Sequences in the Classroom: Effects on Spoken Fluency
Bibliographic record
Abstract
Formulaic sequences (FS) are frequently used by native speakers and have been found to help non-native speakers sound more fluent as well. We hypothesized that explicitly teaching FS to classroom ESL learners would increase the use of such language, which could further result in increased second language (L2) fluency. We report on a 5-week study where students in a control group (n = 8) heard authentic English and practiced speaking and listening using a task-based approach, while students in a treatment group (n = 11) did the same but also focused on noticing and using FS found in weekly topic transcripts. Measures of speech rate (syllables per minute) and mean length of run (number of syllables found in the longest stretch with no pauses) served as objective measures of fluency. Sixteen native-speaker judges assessed excerpts from pre- and posttests for subjective fluency. The number of syllables of FS (expressed as a ratio of the total number of syllables) was counted by two judges. Results found large effect sizes for group membership in all measures, with the treatment group increasing FS use and fluency to a large extent and statistically outperforming the control group on most measures. We conclude that explicitly teaching formulaic sequences may lead to increased use of such phrases and also increased fluency.Les locuteurs natifs emploient souvent des formules; celles-ci font également en sorte que les locuteurs non natifs semblent parler la langue avec plus de fluidité. Nous avons émis l’hypothèse selon laquelle l’enseignement explicit de formules en classe à des étudiants en ALS augmenterait l’emploi de formules, ce qui mènerait à une meilleure compétence en L2. Nous rendons compte d’une étude de 5 semaines pendant lesquelles des étudiants dans un groupe témoin (n = 8) ont écouté de l’anglais authentique et se sont pratiqués à parler et à écouter selon une approche basée sur les tâches. Les étudiants du groupe expérimental (n = 11) ont fait la même chose mais se sont également penchés sur le repérage et l’emploi de formules dans des transcriptions thématiques chaque semaine. Les mesures du débit de parole (syllabes par minute) et de la longueur moyenne des tronçons (nombre de syllabes dans le plus long tronçon sans pause) ont servi de mesures objectives de la fluidité. Seize juges- locuteurs natifs- ont évalué la fluidité subjective à partir d’extraits tirés de pré-tests et post-tests. Deux juges ont compté le nombre de syllabes des formules (exprimé sous forme de ratio du nombre total de syllabes). Les résultats ont révélé une importante ampleur des effets pour l’appartenance au groupe avec toutes les mesures. L’augmentation de l’emploi des formules et de la fluidité chez le groupe expérimental a été notable et le rendement de ce groupe a été supérieur de façon significative à celui du groupe témoin pour la plupart des mesures. Nous concluons que l’enseignement explicit des formules pourrait mener à un emploi accru de ces expressions ainsi qu’à une meilleure fluidité.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".