Task-Based Language Learning and Beginning Language Learners: Examining Classroom-Based Small Group Learning in Grade 1 French Immersion
Bibliographic record
Abstract
Elementary French immersion (FI) language arts teachers often organize instruction around small learning groups. Students rotate through learning stations/centres and work independently with their peers on L2 literacy skills. This study examined how principles of task-based language teaching (TBLT) can be used and/or adapted to further support beginning L2 learners working independently at various literacy stations. This classroom-based study employed a pragmatic ‘research design’ methodology. Researchers worked alongside Grade 1 FI teachers (n=3) in the development and classroom implementation of language/literacy tasks designed around TBLT principles for use in literacy centres. Data collected included classroom observations in two Grade 1 FI classrooms, samples of students’ work, teacher interviews, and task-based lesson plans. Findings suggest that integrating/adapting TBLT principles to small group independent learning stations was particularly impactful in supporting young beginning language learners with extended language output, peer interaction, learner autonomy, emerging spontaneous language use, and student engagement. Additional instructional focus on corrective feedback, oral communication skills, and focus on form and function were also reported.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".