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Record W1552756503 · doi:10.19173/irrodl.v8i3.426

Cross-Cultural Delivery of e-Learning Programmes: Perspectives from Hong Kong

2007· article· en· W1552756503 on OpenAlexvenueno aff
Andrew Lap-sang Wong

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationPopularityE learningContext (archaeology)Educational technologySociologyPedagogyPublic relationsPsychologyPolitical scienceSocial psychologyGeography

Abstract

fetched live from OpenAlex

The growing popularity of e-learning may pose one of the greatest challenges currently facing traditional educational institutions. The questions often asked are how, rather than whether, to embrace this new form of instructional delivery and how to create an appropriate learning environment for the learners. Educational institutions in Hong Kong have the option of adopting programmes or learning materials developed in other parts of the world for local learners, or not. Such an approach of acquiring learning materials is not without risks in terms of the suitability of materials embedded with cultural contents ‘foreign’ to local learners, or in terms of the suitability of assumptions in the communication context. What are the issues involved in the globalization of education through e-learning? This paper explores – from a critical-dialectical perspective – the implications of globalization on educational policy through cross-border delivery of educational programmes by e-learning, with particular attention given to the threat of cultural imperialism. The paper concludes that Hong Kong seems to be coping with ‘cultural imperialism’ rather well because of its unique history of being a cross-road for East and West, and also with some recommendations to e-learning providers to mitigate the potential damage of cross-cultural delivery of e-learning.

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)0.012
metaresearch head score (Gemma)0.007
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 score0.587
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.077
GPT teacher head0.499
Teacher spread0.421 · 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

Citations23
Published2007
Admission routes1
Has abstractyes

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