Teaching English as a Second Language in the Early Years: Teachers’ Perspectives and Practices in Finland
Bibliographic record
Abstract
Second language (L2) education in the early years has been steadily increasing worldwide. Since second language education at earlier ages is relatively new in many countries, not much research is available regarding teaching practices in this context. Likewise, limited research attention has been directed to teachers’ perspectives on early L2 teaching. This study investigated what characterises teachers’ pedagogical planning, teaching practices and assessment of language learning, and teachers’ perspectives about the opportunities and challenges in early L2 classrooms within the cultural context of the Finnish education system. The data for this study were gathered through an online survey involving teachers of English (n = 49) as a second language in the early years of primary education in Finland. The results show that the teachers based their pedagogical planning on the curriculum, used a variety of L2 tasks and materials, which they often prepared by themselves, and they mostly used observation, instead of formal exams, for assessing the children’s learning in L2. The results revealed that the teachers’ perspectives to the early start for L2 teaching were positive, which stemmed from the children’s enthusiasm for language learning. The teachers draw attention to challenges such as big group sizes, the diversity in children’s skills (e.g., their prior L2 knowledge, social skills, learning capabilities), and the limited availability of teaching materials targeted for young learner groups in L2 education. The findings demonstrate the opportunities and challenges L2 teachers of early learners face in Finland.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".