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Record W3000810965 · doi:10.24908/pceea.vi0.13861

SUSTAINABLE INFRASTRUCTURE: DEVELOPING A RE-USABLE PRODUCTION TOOLKIT FOR EFFICIENT ONLINE COURSE DESIGN

2019· article· en· W3000810965 on OpenAlexafffundvenue
Allison Van Beek, Nadine Ibrahim

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsUniversity of WaterlooUniversity of Toronto
FundersUniversity of Toronto
KeywordsUSableCourse (navigation)Computer scienceDistance educationMassive open online courseProcess (computing)Online courseProduction (economics)Open educational resourcesEngineering managementKnowledge managementWorld Wide WebEngineeringMathematics educationPsychology

Abstract

fetched live from OpenAlex

Distance education has come a long since the days of mailed correspondence, with little interaction and a timely delay. As technology continues to more robustly offer opportunities for instructors and learners to be distributed but still interactive, more options for course structures emerge. Open online course development for distance education is a time consuming process that requires deep thought about personal pedagogical beliefs and an exploration of the technological tools available (for both development and for use in the course). This paper details the development of an online open course that features not only open access to course content and files, but also to the supporting resources that were used by the team involved in developing the course. These resources build what the authors are calling a "production toolkit," which can be put into action by any individual or team embarking on the development of their own course. 
 This paper details the background of the project, an overview of the pedagogy that underpins the project, the tools used to produce the course, and the design decisions used during production. Though specific examples are used from the course, the design principles and resources presented can be used across projects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2019
Admission routes3
Has abstractyes

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