How Long Does It Take English Learners to Attain Proficiency - eScholarship
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».