The Effect of Input Flooding and Explicit Instruction on Learning Adverb Placement in L3 French
Bibliographic record
Abstract
Abstract This paper investigates the effect of an oral input flood and form-focused instruction on the learning of adverb placement in French by learners whose first language is Emirati Arabic. Participants were 24 university students in the United Arab Emirates who were true beginners in French. The treatment group (n = 12) received an input flood and form-focused instruction concerning the position of adverbs of aspect (e.g., parfois, jamais), and quantity (e.g., beaucoup) but the control group (n = 12) did not. Results show that input flooding and instruction were beneficial: with positive adverbs the treatment group produced and accepted significantly more adverbs in the target position, and decreased their non-target placement from pretest to posttest. One third of the participants accepted and the produced target order 100% of the time on the posttest. With negative adverbs both groups improved significantly from pre- to posttest, and were more accurate than they were with positive adverbs. The paper concludes with pedagogical implications for teaching adverbs in input-poor linguistic environments. Résumé Cet article examine les effets d’une exposition accrue à l’input oral et de l’enseignement axé sur la forme sur l’apprentissage du placement des adverbes d’aspect (par ex., parfois, jamais) et de quantité (par ex., beaucoup) en français par des apprenantes dont la langue première est l’arabe émirien. Les participantes sont 24 étudiantes universitaires vivant aux Émirats arabes unis. Elles sont des vraies débutantes en français. Le groupe expérimental (n = 12) a reçu une exposition accrue à l’input oral et de l’enseignement axé sur la forme, contrairement au groupe témoin (n = 12), qui n’en a pas reçu. Les résultats révèlent que l’enseignement explicite et l’exposition accrue à l’input oral ont eu un effet positif : avec les adverbes positifs le groupe expérimental a produit et accepté plus d’adverbes dans la position cible et a diminué son placement non cible du prétest au posttest. Un tiers des participantes du groupe expérimental ont accepté et produit l’ordre cible dans 100 % de leurs réponses dans le posttest. En ce qui concerne les adverbes négatifs les deux groupes ont amélioré leur performance du prétest au posttest d’une façon statistiquement significative. Leurs réponses sont plus justes avec les adverbes négatifs qu’avec les adverbes positifs. L’article conclut en soulignant certaines implications pédagogiques concernant l’enseignement des adverbes dans des environnements d’apprentissage pauvres en input langagier.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".