Employers’ perspectives on co-op student work tasks that support their employability competencies
Bibliographic record
Abstract
Significant pressure exists to ensure university graduates have the requisite employability competencies to successfully transition into the workforce and co-op programs continue to be a widely accepted approach in helping to achieve this. Despite research that suggests both beneficial outcomes and drawbacks to co-op programs, what is not well known, particularly in Canada, is the approach employers take in supporting student development in co-op programs, particularly as they balance student development, their own resources, and the present needs of their organization. Based on the Human Capital pillar of Clarke’s (2018) Integrated Model of Graduate Employability, and an anti-neoliberal perspective, this study created an online survey that investigated employers’ perspectives on select employability competencies in four areas: (1) importance, (2) students’ performance, (3) frequency of assigned relevant work tasks, and (4) amount of time spent engaged in assigned work tasks. Participants of the study were defined as employers of organizations who had formal co-op partnerships with the University of Manitoba and who had supervised at least two co-op work terms, one of which was in the 24 months preceding data collection. Descriptive analysis found that most employers indicated that co-op students perform well in employability competencies they believe are important for recent graduates, most notably, ‘Analytical thinking and problem solving’ and ‘Concern for order, quality and accuracy.’ Similar competencies noted for importance and performance emerged with higher ratings in the number of work tasks assigned and time spent engaged in those work tasks. The overall trend of the data, which emerged through the Likert-type questions and was prominent in the open-ended questions was that, though employers try to balance student needs and interest with organizational goals, they prioritize the needs of the organization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".