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Record W3036526101 · doi:10.24908/pceea.vi0.14183

OPEN EDUCATIONAL RESOURCES IN UNDERGRADUATE OPEN EDUCATIONAL RESOURCES IN UNDERGRADUATE CHALLENGES

2020· article· en· W3036526101 on OpenAlexaffvenueabout
Grant McSorley, Agnes D’Entremont, Johnathan Verrett, Nadine Ibrahim, John Dickinson, Rick Sellens, Deena Salem

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsWestern UniversityUniversity of WaterlooQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsOpen educational resourcesOpen educationEducational resourcesEngineering managementKnowledge managementEngineering ethicsPolitical scienceComputer scienceEngineeringPedagogyWorld Wide WebSociology

Abstract

fetched live from OpenAlex

Open Education Resources are pedagogical resources which are available under open licences for reuse and remixing. These resources support collaborative development of education material, the ongoing evolution and improvement of the material and easy access to both educators and students. Several initiatives exist for OER in Canada, and resources specifically targeting engineering are beginning to emerge. At the moment, those efforts are fragmented. In line with the mission to the Canadian Engineering Education Association (CEEA) OER SIG, this paper presents an overview of current Canadian and international Engineering OER initiatives. Based on the findings, several challenges and opportunities pertaining to engineering OER are identified and recommendations are provided for engineering instructors and institutions who wish to increase the use of OER in engineering programs. For instructors, this could be adapting OER where available. For those looking to develop OER, there may be grants and resources at the institutional and provincial levels to support this. For institutions, this may be supporting instructors in using or developing OER through grants or recognition.

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.

How this classification was reachedexpand

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.248
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes3
Has abstractyes

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