How Long Does It Take English Learners to Attain Proficiency - eScholarship
Bibliographic record
Abstract
One of the most commonly asked questions about the education of language minority students is how long they need special services, such as English-as-a-Second-Language (ESL) and bilingual education. Under the U. S. Supreme Court’s interpretation of the Civil Rights Act in Lau v. Nichols (1974), local school districts and states have an obligation to provide appropriate services to limited-English-proficient students (in California now referred to as EL or English learner students), but policy makers have long debated setting time limits for students to receive such services. The purpose of this paper is to pull together findings that directly address this question. This study reports on data from four different school districts to draw conclusions on how long it takes students to develop oral and academic English proficiency. Academic English proficiency refers to the ability to use language in academic contexts, which is particularly important for long-term success in school. Two of the data sets are from two school districts in the San Francisco Bay Area and the other two are based on summary data from reports by researchers in Canada. The data were used to analyze various forms of English proficiency as a function of length of exposure to English. The clear conclusion emerging from these data sets is that even in two California districts that are considered the most successful in teaching English to LEP students, oral proficiency takes 3 to 5 years to develop, and academic English proficiency can take 4 to 7 years. The data from the two school districts in Canada offer corroboration. Indeed, these estimates of the time it takes may be underestimates, because only students who remained the same district since kindergarten were included. While critics of bilingual education have claimed that use of the native language delays the acquisition of English (a claim that is without foundation in the academic literature on bilingualism), it is worth noting that only one of the three districts offered bilingual education. The analysis also revealed continuing and widening gap between EL students and native English speakers. The gap illustrates the daunting task facing these students, who not only have to acquire oral and academic English, but also have to keep pace with native English speakers, who continue to develop their language skills. It may simply not be possible, within the constraints of the time available in regular formal school hours, to offer efficient instruction that would enable the EL students to catch up with the rest. Alternatives such as special summer and after-school programs may be needed. The results suggest that policies that assume rapid acquisition of English – the extreme case being Proposition 227 that explicitly calls for “sheltered English immersion during a temporary transition period not normally intended to exceed one year” – are wildly unrealistic. A much more sensible policy would be one that sets aside the entire spectrum of the elementary grades as the realistic range within which English acquisition is accomplished, and plans a balanced curriculum that pays attention not just to English, but to the full array of academic needs of the students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".