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Record W4386947557 · doi:10.61468/jofdl.v27i1.599

OER-based Online Micro-courses

2023· article· en· W4386947557 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.

Bibliographic record

VenueJournal of Open Flexible and Distance Learning · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOpen educational resourcesWorld Wide WebComputer scienceHigher educationThe InternetEducational technologyDistance educationDigital learningScalabilityOpen educationMultimediaKnowledge managementBusinessEconomic growthSociologyPedagogyEconomics

Abstract

fetched live from OpenAlex

The OER Universitas (OERu) is a consortium of more than 30 higher education institutions on five continents. It was founded in 2011 to provide learners everywhere with learning opportunities and pathways to official recognition or credit. The OERu maintains a very economical base with very low expenses. It supports a sustainable learning environment with incremental increases in infrastructural capacity as and when it is needed. The OERu’s global digital infrastructure has been created to facilitate learner access to micro-courses on the internet from any geographical location. This Next Generation Digital Learning Environment (NGDLE) is based entirely on free and open source software (FOSS). The OERu has established a working model for transnational micro-credentialling for approved university qualifications. To date it has supported more than 200,000 learners in over 100 countries. The OERu is based on a scalable, FOSS NGDLE which has dramatically reduced the cost of providing learning opportunities to anyone, anywhere, on the web.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.042
GPT teacher head0.349
Teacher spread0.307 · 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