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Record W1482073163 · doi:10.19173/irrodl.v14i1.1355

An OER architecture framework: Need and design

2013· article· en· W1482073163 on OpenAlexvenueno aff
Pankaj Khanna, P C Basak

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2013
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
FundersOrganisation de Coopération et de Développement Économiques
KeywordsOpen educational resourcesDistance educationComputer scienceArchitectureKnowledge managementQuality (philosophy)Component (thermodynamics)Relevance (law)World Wide WebMultimediaEngineering managementEngineeringPedagogySociology

Abstract

fetched live from OpenAlex

<p>This paper describes an open educational resources (OER) architecture framework that would bring significant improvements in a well-structured and systematic way to the educational practices of distance education institutions of India. The OER architecture framework is articulated with six dimensions: pedagogical, technological, managerial, academic, financial, and ethical. These dimensions are structured with the component areas of relevance: IT infrastructure services, management support systems, open content development and maintenance, online teaching-learning, and learner assessment and evaluation of the OER architecture framework. An OER knowledge and information base, including a web portal, is proposed in the form of a series of knowledge repositories. This system would not only streamline the delivery of distance education but also would enhance the quality of distance learning through the development of high quality e-content, instructional processes, course/programme content development, IT infrastructure, and network systems. Thus the proposed OER architecture framework when implemented in the distance education system (DES) of India would improve the quality of distance education and also increase its accessibility in a well-organised and structured way.</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)0.002
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: none
Teacher disagreement score0.825
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.079
GPT teacher head0.427
Teacher spread0.348 · 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 designOther design
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

Citations22
Published2013
Admission routes1
Has abstractyes

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