Language Choice Among Peers in Project-Based Learning: A Hong Kong Case Study of English Language Learners’ Plurilingual Practices in Out-of-Class Computer-Mediated Communication
Bibliographic record
Abstract
Recently there has been considerable interest in the role of first language use in second/foreign language learning, especially where students share a common first language. However, most research has focused on in-class interaction between teachers and learners. Much less attention has been given to students’ out-of-class practices, for example, in collaborative project-based learning. To fill this gap, the article tracks the out-of-class activities of 16 students (four project groups) involved in project work on a course in English for science students at an English-medium university in Hong Kong. An analysis of students’ computer-mediated interactions (Facebook, WhatsApp and email) shows that these interactions are plurilingual, with students drawing on English, Chinese and mixed code to different extents as they go about their project work. Different languages are used strategically: whereas L2 is used more in the construction of the final project product, L1 is used more to promote group cohesion. The findings suggest that, in plurilingual contexts like Hong Kong, it is necessary to develop an English language pedagogy that acknowledges the need for the constructive but judicious use of translanguaging and plurilingual practices as students are engaged in L2-focused (e.g. EAP) project-based group work.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".