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Record W1996842502 · doi:10.4018/jicte.2013010103

Transformative Learning and Technology in Adult and Vocational Education

2013· article· en· W1996842502 on OpenAlexaff
Viktor Wang, Patricia Cranton

Bibliographic record

VenueInternational Journal of Information and Communication Technology Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTransformative learningVocational educationAdult educationEmerging technologiesAdult LearningEducational technologyWork (physics)PsychologyPedagogyBlended learningInformation technologyTechnology integrationSociologyPublic relationsPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Adult learners often fear that they will be unable to find work if they are lacking in technological skills. The media, including newspapers, magazines, and advertisements for positions often emphasize the importance of the use of technology in the workplace. It is the case that without adequate skills in the use of appropriate technologies, adult workers may face challenges in finding employment. But what technologies can do is to provide powerful teaching and learning strategies. This article addresses technological applications in vocational and adult education advancement from different perspectives. Technology has the potential to support transformative learning. Technology, along with the vital role of adult educators, helps learners grow, change, and develop. Through the discussion of these, and related issues, a model titled Learners’ Seeking Transformation via Web 2.0 Technologies, has emerged.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.020
Scholarly communication0.0070.005
Open science0.0000.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.294
Teacher spread0.291 · 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 designTheoretical or conceptual
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

Citations13
Published2013
Admission routes1
Has abstractyes

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