Value of Open Microcredentials to Earners and Issuers
Bibliographic record
Abstract
While microcredentials and open digital badges have become increasingly popular in education, more research is needed to better understand their implementation and benefits to both issuers and users. In this paper, we use a case study approach to report and discuss the outcomes from the implementation of an open badges program at National Instruments, highlighting the effects this program has had on both National Instruments and its users. As the program evolves to better meet the needs of its stakeholders, we find that both participants (badge earners) and the issuer (National Instruments) see potential value in the National Instruments Badging Program. The value for both seems to stem from the way in which the program enables the sharing of badges, which helps the earner establish their skills/reputation while also increasing awareness of the program for National Instruments. This study adds to our understanding of why an organization may find value in offering open microcredentials as an alternative to traditional professional development and certificates for their customers and employees.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".