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Record W2979546434 · doi:10.29173/iasl7193

Professional work and learning with smartphones: a comparative study of school librarians from Australia, Hong Kong and United Kingdom

2016· article· en· W2979546434 on OpenAlexvenueno aff
Zvjezdana Dukić, Annie Tam

Bibliographic record

VenueIASL Annual Conference Proceedings · 2016
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Social mediaProfessional developmentSample (material)Medical educationMobile devicePublic relationsPsychologySociologyLibrary sciencePedagogyPolitical scienceEngineeringWorld Wide WebComputer scienceMedicine

Abstract

fetched live from OpenAlex

The study explores the role of mobile technologies in school librarians‟ daily work and professional learning and development. The aim of the study is to examine how school librarians search, access, use and share information via their smartphones, what smartphone functionalities they use for these purposes, and what apps and social media are most frequently used. The study is based on empirical research conducted on a sample of school librarians/teacher librarians in Australia, Hong Kong and United Kingdom. Data were collected with online survey. The major finding of this study is that school librarians in Australia and Hong Kong widely use smartphone technologies for their daily needs and for professional work and learning. Various smartphone functionalities tools are used to satisfy these varied needs. Also school librarians from Hong Kong use various smartphone functionalities more frequently than study participants from Australia and UK. Barriers to smartphone use are also discussed but it seems that they do not detract school librarians from using smartphone technologies for their daily professional activities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.063
GPT teacher head0.303
Teacher spread0.240 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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