Pedagogical strategies for teaching literacy to ESL immigrant students: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Many countries rely on immigrants for population growth and to maintain a skilled workforce. However, many such immigrants face literacy-related barriers to success in education and in the labour force. AIMS: This meta-analysis reviews experimental and quasi-experimental studies to examine strategies for teaching English literacy to immigrant students. METHOD: Following an exhaustive and systematic search for studies meeting pre-determined inclusion criteria, two researchers independently extracted data from 26 English as a Second Language (ESL) studies involving 3,150 participants. These participants consisted of ESL immigrant students in kindergarten through grade 6 who were exposed to English literacy instructional interventions. Measured outcomes were reading and writing. RESULTS AND CONCLUSIONS: Mean effect sizes vary from small to large, depending on instructional interventions and outcome constructs. Across several different grade levels, settings, and methodological features, pedagogical strategies used in teaching ESL to immigrant students are associated with increased competence in reading and writing. Collaborative reading interventions, in which peers engage in oral interaction and cooperatively negotiate meaning and a shared understanding of texts, produced larger effects than systematic phonics instruction and multimedia-assisted reading interventions. The results show that the pedagogical strategies examined in this meta-analysis produced statistically significant benefits for students in all grade levels. The findings also show that students from low socio-economic status (SES) background benefit from ESL literacy interventions. However, significant heterogeneity was found in each subset. Educators and policy makers are encouraged to consider specific school contexts when making decisions about optimal pedagogical strategies. It is possible that contextual factors as well as ESL learner characteristics may influence the effectiveness of these strategies. To ensure literacy acquisition for immigrant students whose primary language is not English, it is important to continue to research successful literacy practices in ways that better inform educators and policy makers.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".