Teaching English to Young Learners: Second Language Acquisition or Foreign Language Learning? – A Case Study
Bibliographic record
Abstract
A well-known, long-standing and heated debate across the literature concerning applied linguistics is whether within the classroom teachers are dealing with second language acquisition or second language learning. This controversial issue is especially relevant for contexts where English is learned as an English as an Additional Language (EAL) as in Portugal. This is particulalry important at a national level, where English is currently taught as a compulsory subject across primary education, with minimal input.However, the English language curriculum, its pedagogy, and assessment are not anchored on Content for Language and Integrated Learning (CLIL) principles, hence resorting mainly to the students’ mother tongue. As we move towards identifying and synthesizing best pedagogical practices for Teaching English to Young Learners, it is key that we seek a deep understanding of the most effective teaching strategies to foster second language acquisition. As the matter of fact, second language acquisition and overall literacy development have long been considered key to young learners of English, as a strong and solid primary education is critical to ensuring their long-term academic success (Snow, Burns, & Griffin, 1998). Nonetheless, one major issue concerning primary English langauae teaching is the overwhelming lack of CPD opportunities and research-informed teaching, thus resulting in English language teachers’ unsufficient pedagogical preparation, leading in turn to students’ disengagement within the second language learning process.Therefore, this paper seeks to explore whether exposure to English-only language lessons, through cross-curricular work sets a viable and solid path for second language acquisition in contexts where it is commonly believed that it is only possible to learn English as a set of isolated words.As methodology for the present study there was resort to children’s literature, language games and to an English-only classroom environment to simulate a bilingual education classroom. Within storytelling and cross-curricular work, key vocabulary was taught, hence working the language both at word and sentence level.The findings, based in students’ samples of work, do illustrate pedagogical practices which demonstrate successful second language acquisition, namely morpheme order acquisition, even with minimal exposure to the target language.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".