Additional language learning : effective approaches to supporting newcomers learning English in British Columbia and Spanish in Chile
Bibliographic record
Abstract
This qualitative research study is a comparative study of instructional strategies that subject content teachers in Canada and in Chile use to teach additional language learners. The goal was to identify the most used and effective strategies. The study was framed within the research literature on additional language teaching and learning related to the constructivist view of learning. Qualitative methods were used, including a self-study carried out by the researcher, an open-ended questionnaire, and semi-structured interviews. The self-study consisted of a written journal based on classroom observations in a Canadian middle school, which allowed the researcher to witness the instructional strategies that teachers used in their classes and to reflect on their effectiveness in the additional language learning process. For the questionnaires and the interviews, the participants were six language specialists (three from each country) who responded to questions about the strategies that teachers use to support language learners in the four main skills (listening, reading, speaking, and writing) and the ways they assess student performance in those skills. The data gathered were coded and analyzed in search of patterns and themes oriented to answer the research question. The results of this study showed that teachers were aware of the modifications they needed to implement in the subject-specific, language, and social areas to support language learners by implementing instructional strategies related to differentiated instruction, comprehensible input, and cooperative learning, along with the use of technology to enhance learning experiences. It was also found that given the great number of approaches and instructional strategies available when planning the tasks and assessments, teachers can take and blend the aspects and elements that best fit their learners’ characteristics and learning profiles, a process grounded in the concept of principled eclecticism. It is hoped that the findings of this research help teachers to evaluate the instructional strategies they currently use and to obtain information on the most effective ones to modify and improve their praxis. It is also hoped that the findings can be used to set possible criteria for the implementation of Spanish as additional language programs in Chilean schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".