Turning the tide on disaffection with French? Analyzing pupil motivation to learn French L2: a comparison of CLIL and ‘traditional’ L2 pedagogies in lower secondary
Bibliographic record
Abstract
This paper presents a comparative study of Content and Language Integrated Learning (CLIL) and ‘traditional’ L2 pedagogies involving 13–14-year-old learners of French in three post-primary schools in Northern Ireland. French is the most widely taught L2 in Northern Ireland, where the study of a modern language is compulsory in the first 3 years of lower secondary. Participants in this study sought to improve pupil motivation and increase the uptake of languages at qualification level. Disaffection with language learning in lower secondary and a decline in the uptake of language qualifications are matters of UK-wide concern (British Academy 2019). In Northern Ireland, the proportion of 16-years-olds taking a qualification in French has fallen year-on-year from 45.5% in 2005 to 21.6% in 2018 (Henderson and Carruthers 2022). This pre-/post intervention study explores if there is a relationship between: i) pupil motivation to learn French and type of pedagogic approach (CLIL/non-CLIL); ii) pupil intention to take a qualification in French and type of pedagogic approach. The term CLIL was coined in the mid-90s to denote a European approach to immersive language education which aimed to replicate the successes of immersion bilingual education in Canada (Marsh 2002). UK studies suggest an association between CLIL programmes and positive attitudes towards language learning in lower secondary (Bower 2019; Coyle 2013); however, CLIL provision in the UK is patchy (Dobson 2020) and success is understood to be context dependent (Dobson 2020; Goris et al. 2019, Sylvén 2013). There have therefore been calls for situated CLIL research accounting for context-specific realities (Dobson 2020; Pérez-Cañado et al. 2021). This study introduced CLIL to three participating schools. In each school, an intervention class engaged with a CLIL ecotourism module through French, whilst a comparator class continued to learn French using the school’s regular scheme of work. Pre/post-intervention pupil questionnaires, teacher interviews and post-intervention pupil focus groups gathered data on teacher and pupil perspectives of CLIL as compared to ‘traditional’ L2 pedagogies and explored the relationship between motivation, uptake and the type of pedagogic approach. The findings identify challenges and opportunities presented by CLIL, exposing tensions between how teachers ‘want to’ and ‘have to’ teach, and highlighting issues regarding level of cognitive challenge and relevance of lesson content, particularly for disadvantaged pupils. The conclusion proposes that CLIL pedagogy can facilitate the co-creation of language curricula with learners integrating linguistic concepts with content that is relevant, engaging and learner-centred.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".