Providing controlled exposure to target vocabulary through the screening and arranging of texts
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Notice bibliographique
Résumé
This article considers the problem of how to bring foreign language students with a limited vocabulary knowledge, consisting mainly of high-frequency words, to the point where they are able to adequately comprehend authentic texts in a target domain or genre.It proposes bridging the vocabulary gap by first determining whic h word families account for 95% of the target domain's running words, and then having students learn these word families by reading texts in an order that allows for the incremental introduction of target vocabulary.This is made possible by a recently developed computer program that sorts through a collection of texts and a) finds texts with a suitably high proportion of target words, b) ensures that over the course of these texts, most or all target words are encountered five or more times, and c) creates an order for reading these texts, such that each new text contains a reasonably small number of new target words and a maximum number of familiar words.A computer-based study, involving the sorting of 293 Voice of America news texts, resulted in the finding that a) the introduction of new target vocabulary in each text could be kept to a reasonably small amount for the majority of texts, and b) the number of target vocabulary items occurring fewer than five times could be kept to a minimum when the list of target vocabulary accounted for 96% of the domain's running words, rather than 95%. THE PROBLEM: L1 VERSUS L2 VOCABULARY ACQUISITIONThere is considerable evidence that L1 learners acquire a large amount of their vocabulary through guessing from context (Nagy & Herman, 1987;Sternberg, 1987).The frequency at which the L1 learner encounters words, and the variety of contexts in which words are encountered, ensure that the learner will eventually come across most new words in a context where the word is guessable.Research suggests, however, that foreign language students do not undergo the same rich and varied exposure to vocabulary (Singleton, 1999).As a result, although EFL elementary-level students quickly learn many of the highfrequency words that occur in teaching materials, they experience a breakdown in their ability to guess from context when faced with the much lower frequency words found in unsimplified texts.This is because the low-frequency words found in unsimplified texts make up too large a proportion of those texts.In other words, since there are not enough familiar words in the text for the learner to use as clues, guessing unfamiliar words from context becomes extremely difficult or impossible.The problem, then, is how to expand a student's vocabulary knowledge to the point where he or she recognizes enough of the words in unsimplified texts to be able to guess unfamiliar words from context.Put another way: what is needed is a strategy for bridging the gap between a knowledge of the kinds of high-frequency words found in elementary texts, and a knowledge of the words necessary for the student to be able to resume incidental vocabulary learning.The problem can be broken into two parts: a) Which words are needed in order to bridge this gap?b) Which methods should be used to teach these words quickly and effectively? Sina GhadirianProviding Controlled Exposure to Target Vocabulary
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
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 découle