Two Telecollaborative Contexts for Writing in a Beginner FSL University Program: Achievement, Perceptions, and Identity
Bibliographic record
Abstract
Face-to-face interaction with target language (TL) group members can provide the intensive second language (L2) exposure required to enhance motivation; it improves attitudes towards L2 development, and promotes achievement (Freed, 1995; Warden, Lapkin, Swain, & Hart, 1995). However, face-to-face interaction with TL group members is not always possible. This is especially true for former core French (CF) students who have enrolled in beginner French as a Second Language (FSL) courses at universities in predominantly Anglophone regions of Canada. To address this issue, I designed a mixed-method case study to examine opportunities for providing intensive FSL exposure and enhancing motivation for beginner FSL university learners. The participants were 55 beginning learners of FSL studying at an Anglophone university in Atlantic Canada. To examine intensive FSL exposure, I compared the overall writing achievement over time of 2 groups interacting in a telecollaborative context: (a) a group interacting with younger Francophone Acadians in another province; and (b) a group interacting with classroom peers of similar L2 proficiency. To gain indepth insight into the effects of the telecollaboration, I explored 4 learners’ L2 motivational self-system: (a) perceptions of their prior and current language-learning experiences; and (b) how language-learner identity was shaped by the experiences. The study is based on 5 data sources: writing samples, background questionnaires, stimulated-recall interviews, language-learning autobiographies, and ongoing observations. It is grounded in 5 bodies of knowledge: the Input-Interaction-Output hypothesis within a socio-cultural perspective (Block, 2003), current L2 writing theory, collaborative learning theory, telecollaborative research, and Dörnyei’s (2005) L2 Motivational Self-System Theory. \nQuantitative comparison of overall writing achievement in the 2 telecollaborative writing contexts (using Mann-Whitney U tests) revealed that the comparison group performed better than the treatment group. Qualitative findings, however, demonstrated that the treatment group had more positive perceptions of their language-learning experiences with respect to L2 writing achievement at university, as well as more positive language-learner identities than did the comparison group. Further exploration of language-learner identities from an L2 motivational self-system perspective identified 3 identity shaping characteristics: evolution, demotivation and amotivation, and self-regulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".