Increasing Enrollment in MacEwan University’s Co-op Program for Marketing Majors
Bibliographic record
Abstract
The study aimed to identify barriers to enrolling in the marketing cooperative education program at MacEwan University and to develop strategies that encourage higher enrollment among marketing majors. Research focused on understanding student perceptions and the effectiveness of current enrollment initiatives, including the primary reasons for their low participation. The research design targeted non-co-op Bachelor of Commerce marketing students, employing a probability sampling method to ensure a representative sample. Major findings indicate that a higher likelihood of recommending the co-op program correlates with better knowledge of program details, suggesting that increasing awareness could boost interest. However, the impact of current advertisements on program recommendation is low, indicating the need for improved marketing strategies. Students preferred receiving information through emails and in-class presentations, which are deemed most effective. Despite some support for mandatory work-integrated learning, there is ambivalence toward making the co-op program compulsory, signalling a need for further exploration. Based on these findings, the study recommends establishing partnerships with local businesses for part-time co-op placements to avoid extending graduation timelines. Additionally, it suggests integrating co-op program information and promotional activities into relevant courses to enhance visibility and stimulate student interest. Another recommendation is allowing students to count co-op placements as elective credits, aligning practical experience with academic and career goals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".