Metacognitive Instruction and Interactional Feedback in a Computer-Mediated Environment
Bibliographic record
Abstract
Multiple theoretical frameworks support the notion of interactional feedback as facilitative of second language (L2) development. However, research demonstrates that learners often avoid providing feedback during peer collaborative work, thus failing to take advantage of key opportunities for language learning and development. Recent studies have examined how metacognitive instruction (MI) may be used to explicitly train learners in the provision of interactional feedback, with results showing increased instances of feedback (Fujii et al., 2016) and improved L2 outcomes (e.g., Sato & Loewen, 2018; Sippel, 2019). Building on this work, this exploratory study investigated the effects of MI on intermediate L2 English learners’ (n = 26) provision of interactional features in synchronous computer-mediated communication. Using a pretest-treatment-posttest design, all learners completed three decision-consensus tasks, with learners in the treatment group receiving direct instruction on the benefits of interaction via an instructional video, a practice task, and subsequent whole-class debriefing. The control group completed the tasks without MI. Results demonstrate that learners’ provision of interactional feedback and language-related episodes increased following MI, with qualitative measures indicating learners had positive perceptions of the training and improved awareness of the potential benefits of interactional feedback in computer-mediated communication. De multiples approches théoriques soutiennent la notion de rétroaction interactionnelle comme facilitateur du développement d’une langue seconde (L2). Cependant, les recherches démontrent que les apprenants évitent souvent de présenter une rétroaction pendant le travail collaboratif entre pairs, ne profitan ainsi pas des principales possibilités d’apprentissage et de développement des langues. Des études récentes ont examiné comment l’enseignement métacognitif (EM) peut être utilisé pour former explicitement les apprenants à la rétroaction interactionnelle, les résultats montrant une augmentation des cas de rétroaction (Fujii et al., 2016) et une amélioration des résultats en L2 (par exemple, Sato & Loewen, 2018; Sippel, 2019). S’appuyant sur ces travaux, cette étude exploratoire a examiné les effets de l’EM sur l’offre de fonctions interactionnelles dans la communication synchrone par ordinateur aux apprenants d’anglais de niveau intermédiaire L2 (n = 26). En utilisant un modèle de pré-traitement-post-test, tous les apprenants ont accompli trois tâches de consensus décisionnel, les apprenants du groupe de traitement recevant des consignes directes sur les avantages de l’interaction via une vidéo pédagogique, une tâche de pratique et un compte rendu ultérieur pour toute la classe. Le groupe de contrôle a effectué les tâches sans EM. Les résultats montrent que l’apport d’une rétroaction interactionnelle et d’épisodes liés à la langue par les apprenants a augmenté après l’EM, avec des mesures qualitatives indiquant que les apprenants avaient des perceptions positives de la formation et une meilleure sensibilisation aux avantages potentiels de la rétroaction interactionnelle dans la communication par ordinateur.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".