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Record W2372802135 · doi:10.51357/jei.v1i1.86

Empowering Adult Learners through Blogging with iPads and iPods

2018· article· en· W2372802135 on OpenAlexafffundabout
Anna Augusto Rodrigues

Bibliographic record

VenueJournal of Educational Informatics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsOntario Tech UniversityDurham College
FundersUniversity of Ontario Institute of Technology
KeywordsEmpowermentGovernment (linguistics)LiteracyDigital literacyFeelingAdult literacyPsychologyAdult educationPublic relationsPolitical sciencePedagogyMedical educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Current statistics indicate that 48 per cent of Canadians over the age of 16 struggle with low literacy skills (Canadian Council on Learning, 2008). The federal government has deemed the development of digital literacy proficiencies amongst Canadians a national priority as this country moves toward a digital economy (Government of Canada, 2012). This research project examined whether adult learners with literacy challenges would feel empowered as a result of creating content for a blog through the use of digital technology. The researcher attempted to understand the impact that blogging and using different types of technology could have on an individual’s self-esteem and whether those feelings of empowerment would encourage an adult learner to pursue further education. Although this research project only ran for a period of six days at a literacy program, there was a noticeable difference in how the participants viewed themselves after they created digital content. Further findings from this project also indicated that there is a gap in research dealing with the impact of digital technology on adult literacy.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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 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

Citations1
Published2018
Admission routes3
Has abstractyes

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