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Record W2594289953 · doi:10.1007/s11528-017-0168-2

Perceptions and Uses of Digital Badges for Professional Learning Development in Higher Education

2017· article· en· W2594289953 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTechTrends · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsEducational technologyPerceptionProfessional developmentElectronic learningTechnology integrationHigher educationMathematics educationPsychologyComputer sciencePedagogyMultimediaMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Few instructors in higher education have completed a formal teaching program and, therefore, rely on informal professional development opportunities to enhance their teaching practice. Micro-credentialing in the form of digital badges is one way in which instructors can document their non-credit learning and accomplishments. This mixed methods research study was conducted to gauge participants’ perceptions and anticipated uses of digital badges. Results of the study indicated that many participants had positive perceptions of the badges, finding them authentic and innovative. Some participants had negative or mediocre perceptions of digital badges, finding them less prestigious than a certificate of completion. Badge appearance may have had an impact on perceived credibility. Participants intended on using their digital badges in a variety of ways, such as sharing on social media and job searches. Many found the badges motivating and persevered to complete a program; however, they did not do this solely to earn a badge.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.407
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it