Relationship building in L2 telecollaboration: examining language learner closings in online text-based chats
Bibliographic record
Abstract
Online exchanges among learners are an important form of communication in language learning contexts. This is due to the affordances this medium of communication creates for learning as well as the growing interest in internationalization of curricula. In fact, online communication can foster connections between people, including learners, across the globe. This paper is based on a qualitative analysis of online chat communication between Canadian learners of German and German learners of English. We discuss how these learners negotiate closing sequences in their chats, considering that the patterns for closing sequences may be different in different languages or be culture-specific and learners need to make choices on a spectrum between business focus (i.e. staying on task) vs. personal focus (i.e. addressing personal information). Hence, learners must negotiate interactive patterns that they may not be familiar with in creative ways that establish and maintain interpersonal relationships. Research about online communication helps to better understand learner communication, especially in an international context. In addition, it helps raise language awareness for teacher training and provides impetus for sensitizing teachers and learners to the ways in which interaction works and how relationships are maintained in communication.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".