The Coupling of Second Language Learning Motivation and Achievement According to Gender
Bibliographic record
Abstract
Despite research investigating gender differences in second language motivation, the examination of such differences with a coupling of motivation and achievement evidence is less common. Given that increased motivation is a contributory factor of achievement (e.g., Schmidt et al., 2001) where gender can also be an influencing variable, it is important to examine the influence of gender on motivation and proficiency in second language education. The following article explores the motivation of 87 Grade 6 early French immersion students through the means of a questionnaire, grounded in Gardner’s socio-educational (1985) and MacIntyre’s (1994) willingness to communicate models. Through multiple regression analysis, the questionnaire findings were examined to see, which if any, variables predicted French proficiency as measured through a multi-skills French test. The female participants outperformed the males in French reading, writing and speaking, whereas only one significant difference was found on the questionnaire items (i.e., language awareness). Although the multiple regression analyses showed both increases and decreases in French achievement according to questionnaire items, where there were significant differences being female was associated with increases in French achievement. These findings offer a gateway to further research, as educators strive to offer quality second language education to all.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".