English language immersion and students' academic achievement in English, Chinese and mathematics
Bibliographic record
Abstract
Research has demonstrated that second language immersion is an effective means of facilitating primary school students' second language without undermining competence in their first language. Despite the rapid growth of English immersion (EI) programmes in China, only limited empirical research has been conducted to evaluate students' academic achievement in these programmes. This study addressed three primary research questions regarding EI students' academic achievement represented by English (L2), Chinese (L1) and mathematics. This study was conducted with a group of Grade 2 (n=385), Grade 4 (n=430) and Grade 6 (n=183) students in immersion or non-immersion programmes in three schools in China. Cambridge Young Learners English Tests were employed as the L2 measure. School-issued achievement tests in L1 (Chinese) and in mathematics were also employed. The results show that immersion students, compared with non-immersion students, did better in English at all three grade levels. They also did similarly in Chinese and mathematics at Grades 2 and 4, but better at Grade 6. The findings from this evaluation study demonstrate a complex and developmental picture of students' academic achievement in English, Chinese and mathematics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".