Towards a cross-linguistic pedagogy: biliteracy and reciprocal learning strategies in French immersion
Bibliographic record
Abstract
This dissertation is based on a 7-week classroom intervention in two French immersion classes (Grades 3and 3/4) in two schools that enroll both English- and French-dominant students near Montreal, Quebec. The intervention aimed to bridge the students' first and second languages (L2) through a 'biliteracy' project that linked English and French language arts content and through the instruction of reciprocal language learning strategies designed to help students make language-learning connections with other students.For the biliteracy project, students' English and French teachers read to them from the English and French versions of three picture books. Following each reading, student pairs consisting of one English- and one French-dominant partner engaged in collaborative literacy tasks. In addition, students received eight strategy lessons with the goals of raising their awareness of their L2 production and enhancing its accuracy, while increasing their awareness of themselves and their peers as language-learning resources.Data collection consisted of student and teacher interviews as well as audiotaped interactions of 8 focal pairs (n = 16) as they worked on all collaborative tasks. The study's mixed-methods data analysis was as follows: Transcripts of the interaction data were first analyzed quantitatively in terms of students' (a) focus on language (operationalized as language-related episodes) and (b) use of reciprocal strategies (operationalized as 'asking questions' and 'giving corrective feedback'). The quantitative analysis offered an overall portrait of students' interaction and allowed for a comparison of pair behaviors as well as of individual partners' behaviors. The patterns that emerged in the quantitative data helped guide the subsequent qualitative analysis of the data. The analyses revealed that all recorded pairs engaged in reciprocal strategy use and extensive on-task collaboration. Language dominance and task type both influenced students' interactional behavior to some degree, but the effectiveness of their task and language problem solving was tempered by the extent to which they engaged in additional interactional moves that sought and supported contributions from their partners. Thus, future instruction that teaches students how to collaborate constructively is highlighted as a key element in promoting the success of similar cross-linguistic approaches.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".