Bibliographic record
Abstract
In recent years, web-enabled credentials for learning have emerged, primarily in the form of Open Badges. These new credentials can contain specific claims about competency, evidence supporting those claims, links to student work, and traces of engagement. Moreover, these credentials can be annotated, curated, shared, discussed, and endorsed over digital networks, which can provide additional meaning. However, digital badges have also reignited the simmering debate over rewards for learning. This is because they have been used by some and characterized by many as inherently “extrinsic” motivators. Our chapter considers this debate in light of a study that traced the development and evolution of 30 new Open Badge systems. Seven arguments are articulated: (1) digital badges are inherently more meaningful than grades and other credentials; (2) circulation in digital networks makes Open Badges particularly meaningful; (3) Open Badges are particularly consequential credentials; (4) the negative consequences of extrinsic rewards are overstated; (5) consideration of motivation and badges should focus primarily on social activity and secondarily on individual behavior and cognition; (6) situative models of engagement are ideal for studying digital credentials; and (7) the motivational impact of digital credentials should be studied across increasingly formal “levels.”
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".