Understanding the Experience of International Students in Cooperative Education
Bibliographic record
Abstract
Canadian post-secondary institutions are experiencing significant growth in their international student populations. At the same time, there is a rising demand for work-integrated learning and specifically co-operative education (co-op) programs. The convergence of these topics has created a dramatic spike in the number of international students participating in co-op. The purpose of this research was to learn from the experiences of international students in co-op to provide evidence and recommendations that might help inform higher education and co-op departments in Canada in their approach to supporting and enabling the success of international students. A mixed-method approach was utilized to collect quantitative and qualitative data from international students who had completed co-op at the college level in Ontario. A survey instrument was developed, and semi-structured interviews conducted from a subset of the survey respondents. The results were reported independently on both data sets and then amalgamated into a critical discussion of the findings and relevant literature. The data and findings are presented and organized into themes of preparedness for co-op, experiences during co-op, and the contribution of co-op departments. The recommendations provided call for more research on this topic as well as leveraging experiential learning and alumni and employers in co-op preparatory curriculum. International students are not a homogeneous group and, as such, require tailored supports and interventions, which include individualized services and education such as workshops, one-on-one advising, and the development of tools that enable students to self-identify their readiness and requirements prior to co-op. Lastly, it is recommended that co-op departments are appropriately resourced to meet the needs of international students and enable them to be successful in co-op.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".