The multiple affordances, complexities and limitations of micro-credentials - practitioner voices
Bibliographic record
Abstract
In this paper I analyse the voices of higher and vocational education practitioners and stakeholders in the micro-credentials arena to answer the research question: What are the possible affordances, complexities and limitations of micro-credentials? Micro-credentials are small pieces of recognised learning and assessment (European Commission, 2020) that can function as an agent of change for better or worse (Desmarchelier & Cary, 2022, Gibson et al., 2016, Hanshaw, 2024, McGreal & Olcott, 2022, Pollard & Vincent, 2022, Ralston, 2021, Wilson et al., 2016). There is a gap in the literature on the possible affordances, complexities and limitations of micro-credentials experienced in practice and following the voices of practitioners’ lived experience points bring us to understanding new ways of doing things (Clandinin & Connelly, 2000). My data collection involved semi-structured interviews with ten participants from Aotearoa New Zealand and Canada who were experts or stakeholders in micro-credentialing development. By using Reflexive Thematic Analyses and Qualitative Descriptive Research, I uncover and present themes, which indicate multiple powerful and positive affordances which act as catalysts to micro-credential development, and numerous associated complexities/limitations which act as inhibitors, and investigate the relationship between them. Looking through the lenses of power/knowledge, which is practised in society as a strategy to exert control over others (Foucault, 1980) and disruptive innovations, which create footholds in markets where no market existed, (Christensen et al., 2015), I explore a possible motivational context behind these inhibitors. Finally, I propose how we might better leverage the successful build out of powerful micro-credentials, to the betterment of the human experience.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| 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".