Tandem language learning through a cross-cultural Keypal project
Bibliographic record
Abstract
Patterns of students' language learning were examined through an asynchronous crosscultural bilingual communication project conducted between Japanese university students learning English and Canadian university students learning Japanese.Previous studies on cross-cultural communication projects have reported positive outcomes in providing learners with opportunities for cultural and language learning.However, very few, if any, investigated whether a meaning-focused communication project like ours provides similar opportunities for language learning, and if so, what kind of language learning takes place.With focus on Canadian students' learning of Japanese, the present study addressed these questions through analyses of students' logs and message texts.The results indicated that students have opportunities for all aspects of language learning, including vocabulary, kanji, grammar, and phrase/sentential expressions.However, it was also found that the amount and type of incidental learning may depend on the students' proficiency level.Explicit corrections are noticed but often not understood unless clear explanations are given.We suggest these kinds of communication projects might further promote incidental learning opportunities by better understanding the proficiency levels of the other's second language, as well as by more and better incorporation of clear error corrections possibly accompanied by metalinguistic explanations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".