Exploring the use of metacognitive strategies in the technology-enhanced classroom: the beginner language learner experience
Bibliographic record
Abstract
This action research study was initiated to establish new knowledge about the effects of metacognitive awareness raising amongst early adult foreign language learners. The study explores their use of metacognitive strategies in technology-enhanced classrooms at the first year college level Spanish in Canada. As part of the regular course syllabus, eight participants received explicit instruction in class on strategic learning, as well as on the use of technological tools (iLrn, Moodle, YouTube, Collaborate). The first cycle of the study established rapport between the practitioner-researcher and the learners, providing preparation and support for learning Spanish, employing strategic actions and using digital resources. Analysis of the interactions and reflections on selected collaborative multimodal tasks in cycles two, three and four identified how early foreign language learners process information for a deeper understanding of themselves as language learners and develop autonomous learning strategies. The data collection instruments employed over the four cycles of action research included pre- and post-treatment questionnaires, audio transcripts of participant task interactions, participant post-task self-reflections, practitioner-researcher observations and reflections, and selected interviews. The study established that even at the early stages of their foreign language processing early adult foreign language learners benefit from metacognitive awareness-raising instruction and teacher support. Findings showed that learners used a targeted repertoire of strategies to manage their learning in the technology-enhanced language classroom. There was an observed increase in their self-efficacy and autonomous behaviours. Understanding the learners’ perceptions and experiences was central to the pedagogical knowledge that was gained by the teacher-researcher as a means of informing teaching practice to enhance the beginner language learner experience. The action research design and findings emphasize the importance of the role of the language teacher to be a digitally literate, metacognitively-aware task designer and support guide for the learners. The study has demonstrated that adult early foreign language learners in a contemporary technology-enhanced language classroom benefitted from a holistic approach to developing cognitive and metacognitive strategies in teaching and learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".