MétaCan
Menu
Back to cohort
Record W1649631076 · doi:10.19173/irrodl.v3i2.104

Online Education Systems in Scandinavian and Australian Universities: A Comparative Study

2002· article· en· W1649631076 on OpenAlexvenueno aff
Morten Flate Paulsen

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianHigher educationLearning ManagementPublic relationsCoercion (linguistics)Political scienceManagement systemComparative educationKnowledge managementSociologyComputer scienceEconomic growthEconomicsManagementWorld Wide Web

Abstract

fetched live from OpenAlex

<P class=abstract>This article presents a comparative study of online education systems in Norwegian, Swedish, and Australian universities. The online education systems discussed comprise content creation tools and systems for learning management, student management, and accounting. The author of this article arrives at the conclusion that there seems to be a general lack of integration between theses systems in all three countries. Further, there seems to be little focus on standards specifications such as IMS Global Learning Consortium (IMS) and Sharable Content Object Reference Model (SCORM) in higher education in all three countries. It was found that both Norway and Sweden value the importance of nationally developed learning management systems and student management systems; however, this does not seem to be the case in Australia. There also seems to be much more national coordination and governmental coercion concerning the choice of student management systems used in Sweden and Norway, than is the case in Australia. Finally, with regard to online education, the most striking difference between these three countries is that of economic policy. In Australia, education is considered an important export industry. In Norway and Sweden, however, the export of education does not seem to be an issue for public discussion.</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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
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 head0.176
GPT teacher head0.509
Teacher spread0.333 · 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

Citations33
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicOnline and Blended LearningFrench-language works237,207