Sex and Age Effects on Willingness to Communicate, Anxiety, Perceived Competence, and L2 Motivation Among Junior High School French Immersion Students
Bibliographic record
Abstract
The students who participate in immersion education are an impressive group. In the present study we looked at students in a junior high school in Nova Scotia. In the local area, English is far and away the dominant language, though there are French‐speaking communities within a 2‐hr drive and Canada is an officially bilingual country. Therefore, the students are not in a “foreign” language‐learning environment, but in all probability, they are not likely to encounter spoken French in their daily lives. The students have all the challenges of adolescence to contend with: moving from an elementary to a junior high school in grade 7, the wonders of puberty, growing academic expectations from teachers, demands from school administration to speak only French while at school, and the burgeoning social life of a newly minted teenager. On top of all this, participants in this research are required to give up their well‐developed native language, English, and undertake to be educated in a second language, French. Impressive. The present study reports a cross‐sectional investigation of second language communication among students in a junior high French late immersion program. The effects of language, sex, and grade on willingness to communicate (WTC), anxiety, perceived communication competence, and frequency of communication in French and on attitude/motivation variables are examined globally and at each grade level. Most of these variables have been widely studied among adult learners, most often at the university level. The present study attempted to look at a much younger group to examine the patterns earlier in the language learning process. We found that students’ second language WTC, perceived competence, and frequency of communication in French increased from grades 7 to 8 and that these increases were maintained between grades 8 and 9, despite a drop in motivation between grades 7 and 8 and a steady level of anxiety across the three grades. Gender differences in language anxiety were observed across the three grades. Contrary to our expectations, compared to girls, boys reported more anxiety in grade 9. However, the differences between WTC across the first and second language narrowed as students progressed through the program. [The present article first appeared in Language Learning, 52(3), 2002, 537–564]
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".