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Contrastive Analysis in Language Teaching, Time to Come in from the Cold. (Language Teaching & Learning)

2001· article· en· W133960640 sur OpenAlexaboutno aff
Ron Sheen

Notice bibliographique

RevueAcademic exchange quarterly · 2001
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueFrench Language Learning Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGrammarValue (mathematics)Meaning (existential)LinguisticsContrastive analysisLanguage acquisitionLanguage educationPsychologyFunction (biology)Foreign languageSimple (philosophy)Mathematics educationComputer scienceEpistemologyPhilosophy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract The purpose of this short paper is to first argue that the rejection of the value of contrastive analysis (CA) input in foreign and second language teaching (FSLT) in the 70% and beyond was unjustified on both theoretical and empirical grounds and then, based on classroom-based research, demonstrate the value teachers of using CA in their teaching. ********** Very few teachers in FSLT of any experience would deny the value of some degree of explicit understanding of the grammar of the language they are learning. Though that understanding in some simple cases may be derived from exposure to comprehensible input, there is ample evidence to demonstrate that learners in normal classroom situations are unable to acquire most grammatical knowledge without the benefit of explicit grammar instruction. Furthermore, it is contended here that that understanding may be facilitated by an awareness of the differences between the L1 and the TL. To put this in concrete terms, francophones when asked a question such as How long have lived here for? will tend to reply live here two years, based on the French Je demeure ici deux ans. Similarly, they may say have 15 years and am born in Montreal calquing the French forms. To help learners overcome such problems, CA input enables them to understand the reasons such errors and thereby go some way to avoiding them. In the case of the first example, one of the errors is caused by the fact that depuis may function with both the meaning of for and since. Students need to understand this contrast and then to grasp the fact that for is used a period of time whereas since refers to the point in time at which the period began as in have lived here four years/I have lived here 1995. All language groups engaged in FSLT will make many such L1-influenced errors. Furthermore, though all errors are potentially fossilisable, a number of studies have shown that it is L1-influenced errors which will prove to be the most persistent (See Sheen 1981, Marton 1981, Mukattash 1986). Given then the substantive presence of L1-influenced errors, their tendency to become fossilised, and, assuming the validity of the premise that learners need to understand the nature of that influence in order to overcome it, it is difficult to understand how the field of language teaching and applied linguistics allowed and even encouraged the rejection of CA input in the 70% and has continued to sanction it. In order to help in the understanding of this rejection, I will trace the events which caused it and demonstrate the degree to which it was unjustified. Then, based on this and the description of classroom-based research studies, I will argue the reintegration of CA input into FSLT. Following the success of the Army Special Training Programme (partly based on behaviourist learning principles) in WW II in teaching American soldiers foreign languages, the early fifties saw the development in US universities of a structural teaching approach which was to develop into the audiolingual method (See Brookes 1964). This method revolutionalised and dominated language teaching in the decades following. CA became an integral part of the method as it was argued that habits associated with the L I would interfere with the learning of the TL. Thus, because francophones say J'ai 15 ans, they will tend to say have 15 years in English rather than I'm 15, at least in the case of beginners. Thus. CA was exploited in order to identify those linguistic habits of the LI which were different from the TL and which might, therefore, cause errors. This then informed material writers as to the forms which would require special attention in the repetition and memorisation drills which characterized the audiolingual method. Furthermore, it was argued that the large majority of errors were caused by L1 influence and that they could be predicted by CA. …

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)0,002
score de la tête « metaresearch » (Gemma)0,004
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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,025

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,010
Communication savante0,0050,004
Science ouverte0,0000,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,338
Écart entre enseignants0,325 · 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
GenreEmpirique

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

En bref

Citations1
Publié2001
Routes d'admission1
Résumé présentoui

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