Cultural Influences on Chinese Students' Asynchronous Online Learning in a Canadian University
Bibliographic record
Abstract
This study explored six Chinese graduate students’ asynchronous online learning in a large urban Canadian university. Individual interviews in Mandarin elicited their perceptions of online learning, their participation in it, and the cultural factors that influenced their experiences. In general, the participants had a positive attitude towards online courses. They perceived that online learning had both advantages and disadvantages for their studies in Canada. The study revealed some features of their participation in online courses, particularly the factors that affected their contribution of messages and their personal experiences. The study also identified six cultural factors that affected their online learning. These findings prompted some suggestions for Western instructors in online courses. Résumé Cette étude explore l’apprentissage en ligne asynchrone de six étudiants gradués chinois dans une grande université urbaine canadienne. Des entrevues individuelles en mandarin rendent leurs perceptions de l’apprentissage en ligne, leur participation à ces cours et les facteurs culturels qui ont influencé leurs expériences. En général, les participants avaient une attitude positive envers les cours en ligne. Ils considéraient que l’apprentissage en ligne avait des avantages et des inconvénients pour leurs études au Canada. L’étude a révélé certaines caractéristiques de leur participation dans les cours en ligne, particulièrement les facteurs qui affectent leur contribution en messages et leurs expériences personnelles. L’étude a aussi permis d’identifier six facteurs culturels qui affectent l’apprentissage en ligne. Ces résultats suggèrent des recommandations pour les formateurs en ligne occidentaux.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".