Teaching Formulaic Sequences in an English- Language Class: The Effects of Explicit Instruction Versus Coursebook Instruction
Bibliographic record
Abstract
This study investigates the relative effectiveness of different teaching approacheson the learning of formulaic sequences. Three comparisons were made in thisstudy: the effects of explicit teaching of formulaic sequences versus teaching embeddedin traditional coursebook instruction, the effects of the degree of salienceof the sequences in the coursebook on learning, and the effects of explicit teachingof formulaic sequences with context versus teaching without context. Sixtynineformulaic sequences occurring in an English as a Foreign Language (EFL)coursebook were selected for the study. The participants were 60 low-proficiencyuniversity students majoring in technology in Vietnam. Participants were quasirandomlyassigned to one of three groups: control, no-context learning, andsentence-context learning. Learning was measured by two multiple-choice testsof receptive knowledge of form and meaning. Findings indicated that althoughexplicit instruction was effective, the degree of salience in traditional coursebookinstruction had no significant effects on learning formulaic sequences. Explicitteaching combined with incidental exposure to formulaic sequences in thecoursebook was superior to the traditional coursebook instruction approach in theclassroom setting. Furthermore, the results from explicit instruction with contextsentences did not differ significantly from those of instruction without context.Explanations for the findings and pedagogical applications are offered.Cette étude porte sur l’efficacité relative de différentes approches pédagogiquesvisant l’enseignement de formules. Trois comparaisons ont été effectuées: les effetsde l’enseignement explicit de formules comparativement à l’enseignementtraditionnel dans le cadre de cours basés sur un manuel de classe; les effets surl’apprentissage du degré de pertinence des formules du manuel; et les effets de l’enseignement explicit de formules avec un contexte comparativement à l’enseignementsans contexte. D’un manuel d’anglais langue étrangère, on a tiré soixanteneuf formules pour notre étude. Soixante étudiants à l’université, avec un basniveau de compétence et poursuivant une majeur en technologie au Vietnam, ontparticipé à l’étude. Les participants ont été assignés, de façon quasi-aléatoire, àun de trois groupes: témoin, apprentissage sans contexte et apprentissage aveccontexte. L’apprentissage a été mesuré avec deux tests à choix multiples portantsur les connaissances réceptives de la forme et du sens. Les résultats indiquentque si l’enseignement explicit est efficace, le degré de pertinence de l’enseignementtraditionnel avec un manuel n’a eu aucun effet significatif sur l’apprentissage desformules. L’enseignement explicit combiné à l’exposition accidentelle aux formules dans le manuel de classe était supérieur à l’enseignement traditionnel basé sur un manuel de classe. De plus, il n’y a pas eu de différences marquées entre les résultats de l’enseignement explicit avec un contexte et ceux de l’enseignement sans contexte. Nous offrons des explications pour les résultats ainsi que des applications pédagogiques.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".