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Enregistrement W21529912 · doi:10.1016/j.theriogenology.2011.03.011

The Universal Language: The Number of Immigrant Students in US Schools Has More Than Doubled in the Past 15 Years. in Response. Teachers Are Broadening Their ESL Programs with the One Tool That Translates in All Dialects-Computer Technology

2007· article· en· W21529912 sur OpenAlexfundno aff
John K. Waters

Notice bibliographique

RevueT.H.E. Journal Technological Horizons in Education · 2007
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation Systems and Policy
Établissements canadiensnon disponible
Organismes subventionnairesSaskatchewan Health Research Foundation
Mots-clésMathematics educationCurriculumActive listeningReading (process)Computer sciencePronunciationPopulationPedagogyPsychologySociologyPolitical scienceLinguistics

Résumé

récupéré en direct d'OpenAlex

FEW TRENDS HAVE changed the demographics of US elementary and secondary schools as dramatically as the record-high immigration of the past dozen years. Students who are learning English for the first time, better known as English language learners, make up greater proportion of the K-12 population than ever before. According to the National Clearinghouse for English Language Acquisition and Language Instruction Educational Programs, between 1989-1990 and 2004-2005, enrollment of ELL students in US schools increased 150 percent, from roughly 2 million to well over 5 million. Mix 5 million-plus ELL students with the demands of the No Child Left Behind Act, which holds schools accountable for the academic performance of limited-English-speaking students, and you have sure recipe for boom in the market for tools and technologies that help teach English as Second Language. are seeing an explosion in this market, says Kristina Potter, senior director of ELL curriculum development at Pearson Digital Learning, provider of preK-12 digital learning solutions. But it's not much that there's bunch of new technologies out there for ESL. It's that more teachers are using computers and software in their ESL programs, and vendors are exploiting the capabilities of existing technologies in ELL-focused products. Specialized ESL software is designed to help ELL students develop English-language listening, speaking, and reading skills. They range from simple, self-directed pronunciation programs delivered on CD, to complete multimedia software suites, such as Pearson's English Language Learning and Instruction System (ELLIS) product line, which can be deployed on desktop PCs, installed on district servers, or delivered via web browser. common thread among these programs is their emphasis on making text-heavy information more accessible through graphics, animation, and video. Virtually all of them offer some level of interactivity, and growing number are web-based or network-connected. best of them are providing more than pretty pictures. buzz phrase here is context-based instruction, which puts students into lifelike situations using digital video. All of our instruction materials are video-based, says Potter, so they situate the students in real-world environments and break that video base down into specific skill instructions. software suites in Pearson's ELLIS line are good examples of ESL programs that exploit the multimedia capabilities of modern computer environments. Users are treated to high-quality graphics, digital sound, voice recording, video, animation, and interactive point-and-click screen controls. line is billed as a complete, interactive, multimedia, customizable curriculum. It includes ELLIS Kids, for young learners at three English-proficiency levels (preliterate, beginner, and low intermediate); and ELLIS Academic, for students 12 and older. (ELLIS Business for working adults is also available.) As Pearson puts it, Learners experience virtual world, using and speaking English. Depending on the level of play, the programs offer interactive role-playing, context-sensitive translation, grammar, vocabulary, cultural insight, pronunciation and comparison tools, mastery tests, and skills-tracking features. For Rosetta Stone, one of the bigger players in the electronic language-instruction business, context is key. company's programs serve as medium for teaching model known as Dynamic Immersion, in which the learner's native language--whatever it may be--is never used for explanation or translation. In effect, we create an environment and set of processes that mimic the way you learned your first explains Duane Sider, the Harrisonburg, VA-based company's director of learning. The environment provides native speakers, real texts, and thousands of real-life images. We introduce you to words in the new language, not by defining them with words in another language, but with the objects and events themselves. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil0,116

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0010,001
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0350,005

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.

Tête enseignante Opus0,035
Tête enseignante GPT0,356
Écart entre enseignants0,321 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2007
Routes d'admission1
Résumé présentoui

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