“It was like a mental Erasmus!” Perceptions of language learning and intercultural understanding in an e-tandem virtual exchange
Bibliographic record
Abstract
Previous research focusing on e-tandem virtual exchanges where learners practice each other’s languages has examined mainly aspects related to form-focused interaction, linguistic development, and intercultural competence (see Akiyama & Cunningham, 2018, for an overview). The present paper examines learners’ introspective data about the benefits they obtained from participating in a Spain-Canada e-tandem virtual exchange. Particularly, the participants’ alternating roles as language learners and language experts and their intercultural understanding were examined to provide insights into their evaluation of the experience of taking part in the exchange. Introspective data was collected on how this exchange helped learners theorize about language learning and teaching and about their intercultural understanding. Learners highlighted that focusing on communication, having patience, and collaborating and cooperating with others were their most frequent concerns when they reflected on their role as language experts helping others practice their dominant language. The findings also indicate that learners’ perceptions about the intercultural understanding gained during the virtual exchange show appreciation of multiculturalism and a tendency to speak about one’s own culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".