Immigrant and refugee students’ achievement in Vancouver secondary schools: an examination of the common underlying proficiency model
Bibliographic record
Abstract
The purpose of this study was to investigate the effect of first language literacy and educational backgrounds on literacy and academic performance in a second language and, to learn more about students' perceptions of their linguistic, academic and social development in schooling in which the language of instruction is English. Fifty-five students were selected from seven high schools in the Vancouver School District, Vancouver, British Columbia. Information about students' first language (L1) literacy and educational experiences, including previous instruction in English was obtained on arrival. Proficiency in second language (L2) reading and first and second language writing was observed on arrival and in the spring of 1996, after a minimum of four years of English-only schooling, using standardized and holistic measures. Grade Point Averages (GPA) were calculated for students' achievement in four academic subjects. Analysis by ANOVA showed a significant difference in the length of time spent in ESL due to years of previous English study (F (7,43) = 4.26, p = .0012). Pearson product-moment correlation coefficients were calculated to observe relationships between L1 literacy and time spent in ESL, L1 education and time spent in ESL, and L2 reading and writing and achievement in English, social studies, science and math. Significant relationships were found between proficiency in L2 reading and writing and academic achievement, as measured by GPA. Significant findings were also obtained for L1 literacy and time spent in ESL (-.33, p < .05). Orthographic similarity was not a predictor of L2 reading, as measured on a standardized test of reading comprehension (t = .105, p = .747). Results of the study showed that L1 literacy development, L1 schooling, and previous English study enhanced acquisition of English, as measured by time spent in ESL. The researcher concluded that L1 literacy and education are important factors affecting the rate and level of L2 proficiency attained and academic achievement. Implications from findings suggest that in schooling where the language of instruction is English, students who have not acquired literacy skills in L1 have different needs and face a greater challenge than students who are literate in L1 .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".