Collaborative Online International Learning Classes to Enhance Co-Creation in Canada and Japan
Bibliographic record
Abstract
The coronavirus disease (COVID-19) globally accelerated distance learning. Students who wish to create new businesses pursue collaborative learning in a cross-cultural environment. However, the research on the effect of collaborative learning on such courses is scant. This study investigated the changes in students’ entrepreneurial competencies and cross-cultural knowledge, skills, and abilities before and after participating in an online global career course and the differences between students from three Canadian universities and a Japanese university in collaborative online international learning (COIL). Survey data were collected from June to August 2021, before and after the course, from 33 participants. The questionnaire survey was based on the five main categories of knowledge, problem-solving skills, communication skills, cross-cultural understanding and teamwork skills, and confidence and motivation. The results revealed statistically significant differences in all the categories before and after the course. Considering the effect sizes, all five categories except for confidence and motivation improved following the course, showing that both the individual and the collaborative learning in the course design worked well in the COIL approach. Although most of the students lacked a business background, they could understand the basic frameworks for business planning through self-study in the asynchronous sessions and considered the tasks and solutions in the synchronous collaboration stage. Furthermore, the students from the Canadian universities performed well in all five categories and the students from the Japanese university performed well in four categories. Considering the budget and accessibility, students’ learning outcomes in COIL have a positive effect on their understanding of global careers.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".