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Record W1575788355 · doi:10.56105/cjsae.v27i3.3866

Big hat and no cattle? The implications of MOOCs for the adult learning landscape

2015· article· en· W1575788355 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal for the Study of Adult Education · 2015
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsMcGill UniversityUniversity of Victoria
Fundersnot available
KeywordsAndragogyMassive open online courseOnline learningSociologyPedagogyAdult educationWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Massive Open Online Courses (MOOCs) are a relatively new form of education, offered by some of the best known universities and attracting many hundreds of thousands of students. The authors describe MOOCs and the claims made for them, and analyse two major impacts of these programs based on the first MOOC offered by McGill University. The first area of discussion is the potential for expanded access to education offered by MOOCs, which this analysis finds to be more limited than often claimed. The second is the potential for radical educational practice in MOOCs, which is considered through the lens of andragogy. The authors find that there are indications of interesting possibilities in this area, though they are not being exploited as fully—or as deliberately—as they could be.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.300
Teacher spread0.273 · 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