Etude de la stratégie enseignante d’étayage dans des interactions en classe de français langue étrangère (FLE), en milieu universitaire chinois
Bibliographic record
Abstract
This study deals with the analyses of teaching practices during a controlled experiment in a Chinese university (South China Normal University, Canton, Guangdong, China). We focus on the way French as a Foreign Language (FFL) teachers guide their students during oral sessions and as well on their scaffolding. In a teaching/learning situation, the scaffolding stands for all the ways the teacher adapts his interventions to his students’ abilities as well as his processes of adjustment in a classroom setting. Our aim is to observe and evaluate the strategies used by teachers to improve oral comprehension and oral production. From a methodological point of view, the research relies on various sources of data: the analysis of questionnaires filled in by Chinese students, conversations with the teachers, and observations of classes. Our filmed observations are transcribed (ICOR convention) and analyzed from an interactionist perspective. We focused on two groups of students with a different teacher for each group (one conducted by a Chinese teacher and the other conducted by a French Canadian teacher). We observed two classes for two groups (same teaching materials, equal number of students, same level). The methodology of observation was carried out in three phases: class preparation by the teacher (interview before class and teaching preparation), class progress (teacher’s action) and metacognitive feedback after the class (interview after class). This allowed us to consider the class as a whole but also as a complex series of parts. The analysis grid, which takes into account the verbal, the para-verbal and non-verbal character of speech, is one of the pioneering aspects of this study. With the aid of this analysis grid, we assess the nature of the FFL teachers’ scaffolding and, thus, the way FFL teachers guide their students during oral sessions in the context of a Chinese university.This study is part of a praxeological research aimed at defining pedagogical and didactical guidelines, on one hand about the crucial role of scaffolding in teaching practices and, on the other hand, about FFL oral didactic in a particular setting: Chinese universities.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".