Pedagogical Approaches for Encouraging Interaction Awareness and Interactional Competence in University-Level Second Language Learners
Notice bibliographique
Résumé
With my dissertation, I show how learning about interactional patterns and organizational features of spoken language can be achieved for undergraduate second and foreign language (L2) learners, supported by empirical data and arguments drawn from previous research in L2 pedagogy and Conversation Analysis-Second Language Acquisition (CA-SLA) for integrating interactional learning materials and tasks into undergraduate L2 classroom curricula. Through my study, I examine and evaluate the implementation of learning tasks and activities involving recordings and written transcripts of naturally occurring interactions in the L2 for undergraduate learners to learn about interaction and language. This examination is conducted using Interaction Analysis as a methodological framework of investigation, specifically using a reworked version of Schermuly and Scholl’s (2012) Discussion Coding System (DSC) for Group Interaction Analysis. In order to theoretically ground my research, I propose the understanding of interaction awareness (IA) as a prerequisite for the development of interactional competence (IC), where IA can be understood as learners’ capacity to become aware of aspects that are at the core of IC, such as organizational features and reoccurring interactional patterns, pragmatic and social implications of interactional and linguistic features, as well as non-verbal and prosodic features that are observable in spoken interaction. The focus of my study then concentrates on interactional tasks and learning materials comprising recordings and written transcripts of naturally occurring interactions in the L2 for undergraduate learners of German as a means of encouraging IA and language awareness (LA) by way of processes involving discovery learning (DL) and social learning. With my analyses and discussions, I examine the ways that the learners’ observations and discoveries about features of interaction and language, posited during the recorded learning sessions comprising the dataset of my study, can be conceptualized as IA. The collaborative discovery and meaning construction work enacted by the learners during the recorded language sessions comprising my study allow for a close, empirical investigation of the learners’ processes and methods of conduct for invoking reflection and negotiating understanding about interactional and linguistic features of the L2 encountered in the learning materials, thereby leading to an awareness of interaction and language that can be inferred through the recorded learner interactions. The conclusions drawn from my study findings indicate that the learners demonstrated LA through their discussions about individual lexical items and grammatical concepts, for example, with considerations about observed variations in verbal production of specific lexical items, as well as systematicities in spoken language production of verb conjugations in the L2. The concept of IA pushes this understanding of LA further by borrowing and incorporating elements from Conversation Analysis (CA), as shown when the learners demonstrated their capacity to consider, reflect, and formulate hypotheses about these discoveries within the specific, socially situated contexts of each of the observed interactions. Adding to this, IA can constitute further considerations about interaction and language, for example, cultural, regional, or pragmatic implications comprising specific instances or variations of language use in spoken interaction, or specific processes relating to certain points in the interaction, for example, becoming aware of linguistic and interactional processes and re-occurring patterns to do with positional features of interaction, such as goodbyes and conversational closing sequences.
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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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».