BRIDGING LANGUAGE GAPS OF L2 (SECOND LANGUAGE) TEACHERS BY OPTIMIZING THEIR SELF-AWARENESS
Notice bibliographique
Résumé
"During a Canada-wide consultation session of teacher trainers for future teachers of French, Canada’s official second language (L2), given the problematic situation of unprepared candidates with questionable mastery of the language, some instructors even retreated to a position stating that these students need to be encouraged although they are struggling with French. What this implies is placing role models in classes with inaccurate French, repeating the same situation if not making it even worse as indeed early French immersion is still the chosen protocol by Canadian non-French speaking parents. Young children absorb language like sponges repeating their teacher and if their French is inaccurate, learning the mistakes. What is however of more crucial importance is not to replicate language programs delivery from which learners emerge without sufficient mastery to make themselves understood because of inaccurately learnt language forms. Therefore, we have to uncover remedies to properly guide all learners, through strategies and techniques for their individual management of the language they are trying to acquire-learn. We want to ensure an economy of time in teaching programs with efficient contact times. Revisiting language programme approaches to uncover what was advocated for error correction, we looked at actional attention (Ellis, 1992), work on noticing (Fotos, 1993), markedness (Larsen-Freeman, 2018), interference (Abdullah & Jackson, 1998) interlanguage theory (Selinker, 1972), the monitor model (Krashen, 1982) and recent types of approaches, namely notional functional, communicative, and action-oriented. As well, we gleaned insights from a review of the literature on strategies and techniques including Raab, (1982) on spectator hypothesis with feedback to the whole class; through peer correction by Cheveneth, Chun and Luppesku (1983); with other innovative techniques suggested by Edge (1983); techniques advocated by Vigil and Oller (1976) for oral correction; and correction across modalities (Rixon, 1993). We will report on a qualitative study (Creswell & Poth, 2018) based on an analysis of instructor’s notes regarding the observed effect on some of the strategies that were tried and across different student groups. In this study, notes on how the instructor devised ways of drawing attention and using metacognition to obtain the best results are examined. In addition, ways involving the affective domain, through emotions and also using innovative ways through disruptions etc. were tried to see if they provided a further impact. Students reported that they appreciated the corrective feedback the way it was dispensed. However results show a variety of concerns, namely the problem with deeply fossilized errors, some students’ being over confident about their language ability, and either a deep concern for making errors that is paralyzing or a belief that over time correction will take place in interlanguage development without making any effort. Due to page limitations, in this paper we will essentially present overarching aspects."
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Comment cette classification a été obtenuedéplier
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».