Impact of Performance Feedback for Effective Use of Digital Badges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A recent educational trend has been the increasing focus on validating and credentialing learning taking place outside of traditional academic settings. Digital badges have the potential to address various concerns within formal education settings. Digital badges have been heralded for having the ability to show mastery of content, and more accurately reflect the actual knowledge and skills of learners (Mehta, Hull, Young, & Stoller, 2013). Digital badges have been shown to increase motivation (Light & Pierson, 2014; Lin et al., 2013), increased student learning outcomes (Newby & Cheng, 2019; Wonder-McDowell et al., 2011), and overall comprehensiveness of learning (Mettler, Massey, & Kellman, 2011). Formative assessment through instructor feedback is crucial to mastering content and displaying achievement. Feedback delivers important information regarding desired learning and perceived learning and affords opportunities to decrease that gap. A common misconception is that all digital badges are automated, however, in many teaching and learning settings, feedback is provided by instructors. The authors provide a set of optimal feedback suggestions to aid in Mastery Learning and digital badge instruction and consider the implications these actions in education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it