Perceived impact of extra-curricular activities on foreign language learning in Canadian and Russian university contexts
Bibliographic record
Abstract
This paper surveys language-related extra-curricular activities (ECA) attended by university students in Canada and Russia. Very little information is available about ECA in both countries. The study aimed to gather data about extra-curricular activities in Canada and Russia and to investigate the perceived effect of participation in extra-curricular activities on language learning by university students in these countries. The study employed a questionnaire-based survey as a major research method. The questionnaire constructed by the authors included ‘yes/no’, multiple choice and open-ended questions. The total of 119 university students from both countries participated in the study. The participants’ responses to yes/no and multiple choice questions were entered on SPSS charts for descriptive statistics and an analysis across the groups (chi-square tests). The responses to open-ended questions were analyzed using key-word method. The results indicate that only about 1/3 of university students in both countries had some experience with ECAs. Russian students were more aware of the range of ECAs available through their universities. The array of language-related ECAs was different across the countries: Canadian students mostly attended ECAs that were offered through their universities, and Russian students – outside their universities. There was an agreement between the respondents from both countries that trips abroad were the most efficient form of ECAs. The evaluation of some other specific forms of ECAs showed significant differences across the two participant groups. The majority of respondents from both countries placed a high value on ECAs and thought that ECAs were beneficial for their language skills development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".