Corrective Feedback and Multimodality: Rethinking Categories in Telecollaborative Learning
Bibliographic record
Abstract
Teletandem (Telles, 2009) is a model of telecollaboration in which pairs of foreign language students from different countries meet regularly and virtually to learn each other’s languages. Within this context, participants are expected to help their partners learn by providing feedback. The multimodal nature of this type of environment, however, may offer different learning opportunities (Guichon & Cohen, 2016) and have an impact on feedback provision. This research aims at investigating peer corrective feedback in Teletandem in relation to the different modes. Using a case study approach, we describe how three Brazilians offered feedback to learners of Portuguese as a foreign language. Data used came from 20 Teletandem oral sessions that took place over a period of three years and were stored in MulTeC (Aranha & Lopes, 2019). Data analysis revealed that CF provision is characterized by reformulations, with a blurred distinction between recasts and explicit corrections due to a combination of multimodal strategies. Results also indicate that error correction may be more (or less) emphasized depending on how interlocutors combine multimodal resources. Pedagogical implications are discussed.
 Teletandem (Telles, 2009) est un modèle de télécollaboration par lequel des paires d’étudiants de langue étrangère originaires de différents pays se rencontrent régulièrement de façon virtuelle pour apprendre les langues des uns des autres. Dans ce contexte, on s’attend à ce que les participants aident leur partenaire à apprendre en leur fournissant de la rétroaction. La nature multimodale de ce type d’environnement peut, cependant, offrir diverses occasions d’apprentissage (Guichon & Cohen, 2016) et influencer la façon dont la rétroaction est fournie. Le but de cette recherche est d’étudier la rétroaction corrective par les pairs dans Teletandem par rapport aux différents modes. En se servant d’une approche par étude de cas, nous décrivons comment trois Brésiliens ont offert de la rétroaction à des apprenants de portugais langue étrangère. Les données utilisées venaient de 20 sessions orales de Teletandem qui s’étaient déroulées sur une période de trois ans et ont été stockées sur MulTeC (Aranha & Lopes, 2019). L’analyse des données a révélé que la fourniture de rétroaction corrective se caractérise par des reformulations, qui ne distinguent pas très bien entre des refontes et des corrections explicites, ce qui est dû à une combinaison de stratégies multimodales. Les résultats indiquent également que la correction des erreurs peut être plus ou moins soulignée selon la façon dont les interlocuteurs combinent les ressources multimodales. On discute des implications pédagogiques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".