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Record W2064789896 · doi:10.5539/ass.v8n16p45

Creation of a Knowledge Society via the Use of Mobile Blog: A Model of Integrated Meaningful Hybrid E-training

2012· article· en· W2064789896 on OpenAlexvenueno aff
Rosseni Din, Helmi Norman, M. Faisal Kamarulzaman, Parilah M. Shah, Aidah Abdul Karim, Nor Syazwani Mat Salleh, Mohamad Shanudin Zakaria, Khairul Anwar Mastor

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsConfirmatory factor analysisKnowledge managementLearning stylesComputer scienceLifelong learningTraining (meteorology)Information and Communications TechnologyTacit knowledgePsychologyStructural equation modelingMathematics educationPedagogyWorld Wide WebMachine learning

Abstract

fetched live from OpenAlex

Mobile blog or “moblog” provides a coherent purpose for strategic educational change through lifelong education and the creation of a knowledge society. Many studies have been conducted on using conventional blogs in teaching and learning yet only few have focused on moblogs. Moblogs allow trainers to empower themselves through the acquisition of both explicit and tacit knowledge. Thus, this study aims at designing, developing and implementing moblogs as a platform for a new hybrid e-training approach, which was tested to generate a two-stage model for meaningful hybrid e-training. The data were collected from 213 Information Communication Technology (ICT) trainees which were subsequently tested using confirmatory factor analysis with AMOS 7.0 to obtain the best-fit measurement model for an integrated meaningful hybrid e-training model using moblogs. The results revealed that: (i) hybrid e-training influenced the achievement of meaningful e-training; (ii) learning styles influenced the acceptance of hybrid e-training; and (iii) learning styles preferences influenced the achievement of meaningful e-training.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.061
GPT teacher head0.342
Teacher spread0.281 · 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.

Study designQualitative
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

Citations9
Published2012
Admission routes1
Has abstractyes

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