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Record W1551198897 · doi:10.19173/irrodl.v12i2.916

Head of gold, feet of clay: The online learning paradox

2011· article· en· W1551198897 on OpenAlexaffvenue
Thomas Michael Power, Anthony Morven-Gould

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConceptualizationMainstreamChampionFunction (biology)Quality (philosophy)Distance educationAttritionPsychologyPublic relationsComputer sciencePolitical scienceMathematics educationArtificial intelligenceMedicineLawEpistemology

Abstract

fetched live from OpenAlex

Although online learning (OL) is becoming widely accessible and is often viewed as cutting-edge, the actual number of regular faculty participating in this form of teaching remains small. Moreover, OL, despite its growing recognition, is often associated with high rates of student dissatisfaction and isolation, withdrawal, and attrition. Furthermore, although administrators typically champion support of OL, they often seem unable or unwilling to marshal the necessary financial, human, and technological resources to produce high-quality course materials and to effect efficient course delivery. In short, online learning seems paradoxically to be both booming and busting simultaneously. It is expanding supply yet hitting similar obstacles that distance education encountered generations earlier. Under these circumstances, OL is unlikely to become mainstream without a major redirection. This article applies economic principles and concepts to OL. The revised conceptualization posits that an understanding of stakeholder priorities is the key to improved online course design and delivery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.012
Scholarly communication0.0090.020
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.173
GPT teacher head0.495
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations71
Published2011
Admission routes2
Has abstractyes

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