Preferences for Micro-credentials Offered by Canadian Colleges and Universities
Bibliographic record
Abstract
Micro-credentials are certifications earned for acquiring specific skills or knowledge. They may be useful to earners for their relevance and immediacy in addressing learning needs. When offered by post-secondary institutions, micro-credentials can provide learners with flexible and focused learning pathways to employment or further education. . Noticeably absent from scholarly and public discourse on micro-credentials offered by traditional post-secondary institutions is empirical evidence about the factors potential learners of these new credentials find appealing and important. This dissertation in practice investigates the attributes that potential learners find appealing in micro-credential programs offered by accredited post-secondary institutions in Canada. Using a discrete choice experiment (DCE), the study surveyed 1,005 Canadian respondents aged 24 to 64 to understand their preferences and willingness to pay for various program attributes. These attributes included the format of instruction, total hours to complete, time commitment, provider reputation, connections to employers and other programs, availability of reimbursement, and program cost.The analysis used conjoint, latent class, and market simulation methods to explore aggregate, individual, and subgroup preferences. Subgroups were formed based on similar decision profiles and stated preferences rather than socio-demographic data. Results indicated that enrollment preferences are influenced by program cost, provider reputation, employer connections, and instructional format. Reimbursement availability, total completion hours, and time commitment were also factors, but to a lesser extent. Four latent class segments were identified, showing significant variations in preferences, particularly regarding employer connections and in-person versus online learning. Practical implications of potential learners’ preferences were demonstrated through market simulations, predicting different enrollment rates and willingness to pay among the four subgroups. This study is the first to apply DCE methodology to investigate micro-credential program preferences, providing insights into adult learner preferences and discussing their implications for program design. By quantifying preferences for various program attributes, this research establishes a national empirical baseline that informs post-secondary institutions on how to tailor micro-credential offerings to meet the diverse preferences of adult learners and serves as a foundation for future empirical research in this field.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".