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Record W2749550092 · doi:10.19173/irrodl.v18i5.2751

Massive, Open, Online, and National? A Study of How National Governments and Institutions Shape the Development of MOOCs

2017· article· en· W2749550092 on OpenAlexvenueno aff
Cathrine Tømte, Arne Martin Fevolden, Siri Aanstad

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)MediationPolitical sciencePublic relationsGovernment (linguistics)Diversity (politics)GlobeNational developmentSociologyPsychologyEconomic growthSocial scienceGeography

Abstract

fetched live from OpenAlex

<p class="3">We explore interpretations of MOOCs around the globe and, in particular, interpretations of MOOCs in Norway. Based on a review of previous studies relevant to these topics, we present two contrasting views on the emergence and development of MOOCs, namely the global interruption view and the national mediation view. We suggest, based on previous studies that MOOCs seem to follow national paths more than global paths. In order to grasp the diversity of understandings of MOOCs, we developed a framework that embraces various aspects of motivation, context, and structure regarding MOOCs. With these two polarised views of MOOC development (the global interruption view and the national mediation view), and the framework serving as an analytical approach, we looked at Norway and analyzed the understandings of the development of MOOCs within this particular national context. The national government seems to have been important in the development of the present MOOCs in Norway, both by organizing a particular group of experts in a dedicated commission to consider the future of MOOC in Norway, and by initiating and giving financial support to the development of MOOCs.</p>

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)0.004
metaresearch head score (Gemma)0.004
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 score0.359
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
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 head0.228
GPT teacher head0.497
Teacher spread0.269 · 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 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

Citations21
Published2017
Admission routes1
Has abstractyes

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