Improving Learners’ Oral Proficiency in French Through the Communicative Approach: Colleges of Education in Oyo in Focus
Bibliographic record
Abstract
It cannot be overemphasised that French language is a foreign language in Nigeria and that its teaching and learning cannot take the same process as acquiring/learning the mother tongue or a second language. Foreign language learning requires some strategic applications in order to be able to interact with the native speakers in real life day to day communication. This study aims at delving into some teaching strategies involving the communicative approach to teaching French as a foreign language in order to boost the oral proficiency of our learners in French. The teachers and students in two colleges of Education namely Federal College of Education (Special) [FCES] and Emmanuel Alayande College of Education (EACOED), both located in Oyo town, were the participants in the study. Data were collected through classroom observation, students’ achievement test as well as questionnaires for teachers. The results indicated that students perform better when the teachers employ the communicative approach. Based on the findings of this study, it is therefore recommended that teachers of French language use the communicative language teaching approach to build confidence in their students as this will help to develop faster their linguistic skills, given that this approach gives priority to listening and speaking skills over reading and writing skills.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".