Perceptions, Attitudes, and Choosing to Study Foreign Languages in England: An Experimental Intervention
Bibliographic record
Abstract
The declining interest in foreign languages in English‐speaking countries has been attributed to negative societal attitudes and specific pupil attitudes and perceptions. While various initiatives have aimed to encourage language study, little research has systematically documented the relationship among perceptions, attitudes, and actually opting to study a language (henceforth, uptake). This article reports on an experiment involving 604 13/14‐year‐old pupils in 3 secondary schools in England that tested whether perceived relevance of languages can be improved through 2 different interventions (a panel discussion with external speakers versus a lesson with an external tutor) and whether pupil perceptions and attitudes can be linked explicitly to foreign language uptake at an optional level. Findings show that only pupils who participated in the panel discussion subsequently reported more positive attitudes and higher personal relevance of languages, though participating in either intervention may have contributed to higher uptake compared to a nonactive control group. Boys appeared to have generally more negative attitudes toward both language learning and advocacy than girls. Declared intentions to study or drop a language did not align with final decisions for one quarter of the pupils. Critically, perceptions of language lessons (French, German, or Spanish) and attitudes to languages were reliable predictors of uptake, though the strongest predictor was whether pupils considered languages to be important for themselves. Perceived wider importance of languages was not, in itself, a sufficient incentive.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".