Implementing a competency-based assessment approach to micro-credentials
Bibliographic record
Abstract
Although there is no globally accepted definition to guide micro-credential activity in higher education, many seek to boost the employability prospects of earners. To do this well, micro-credentials need to indicate skills and competencies. Assessment ensures those skills and competencies are verified, enabling trust and communication in the labor market. But, what type of assessment best addresses the career-oriented goals of micro-credential initiatives and learners? This study focuses on a single case in a Canadian university school of continuing education. The purpose of the case is to support practitioners and leaders through a detailed account of one approach to micro-credential program development and the context that surrounds it. The case describes a competency-based approach to micro-credentials with a focus on authentic assessment design with the goal of improving employment outcomes. Operational implications are outlined including policy development, community consultation, backward design processes, team composition, and branding.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".