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Record W3148279292 · doi:10.29173/iasl7875

Moving Beyond Tradition: Technologies Facilitating Students' Cognitive Ability and Modifying Pedagogical Practices of School Librarians in Jamaica and Antigua

2021· article· en· W3148279292 on OpenAlexvenueno aff
Kerry-Ann Rodney-Wellington

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetCognitionSample (material)Emerging technologiesPsychologyMultimediaSchool libraryMathematics educationSociologyPedagogyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

School library collections in Jamaica and Antigua have moved beyond the traditional print-only collection to various technologies that facilitate teaching and learning. Some of these technologies are not directly located in the library but are placed in computer labs where the school librarians have access to them. This research was conducted to determine what types of technologies were used in the libraries studied, how they facilitated students’ cognitive skills, and how they modified school librarians’ pedagogical practices. The sample consisted of 52 school librarians in Jamaica and Antigua. The findings showed that participants had or had access to technologies such as multimedia projectors, interactive SMART Boards, computers with and without internet access, and tablets. These technologies facilitated students’ cognitive behavior by providing them with additional content, and promoted active learning, among other things. Participants’ pedagogical practices were modified as they now deliver instruction using PowerPoint, blogs, Twitter, Facebook, podcasts, and e-books.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.351
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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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