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Record W2799847031 · doi:10.19173/irrodl.v19i2.3431

OER Awareness and Use: The Affinity Between Higher Education and K-12

2018· article· en· W2799847031 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOpen educational resourcesEducational technologyCreativityOpen learningHigher educationOpen educationComputer scienceKnowledge managementMultimediaPedagogyTeaching methodPsychologyCooperative learningWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Educators within Higher Education (HE) and K-12 share in the need for high quality educational resources to assist in the pursuit of teaching and learning. Although there are numerous differences between the two levels of education, there are commonalties in the perceptions of the purpose, practical uses, and challenges that abide in the use of Open Educational Resources (OER). Observations made while producing podcasts and videos for OER awareness, use, and championing, form an exposition of the merits of OER for HE and K-12. Benefits include cost-savings in acquiring resources for teaching and learning as well as user-generated content, instructor creativity, and contextualized and responsively timely learning opportunities. Additionally, the teaching culture of K-12 has historically supported the sharing of learning activities and learning resources. At all levels of education, OER awareness requires a deeper understanding of the changes to teaching and learning borne by open educational practices.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.340
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.158
GPT teacher head0.469
Teacher spread0.311 · 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