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Record W3127034730 · doi:10.29173/iasl7600

Strategy of Promoting E-Learning in School Library

2021· article· en· W3127034730 on OpenAlexvenueno aff
Tzong-Yue Chen

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceWorld Wide WebReading (process)Competitor analysisLiteracyCurriculumThe InternetCONTESTMetadataMultimediaKnowledge managementBusinessMarketingPolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

In 1970, a Fortune magazine announced the names of top 500 enterprises and their performance in the United States. Ten years later, one third of them disappeared. Due to unprecedented technology progress and increase in competitiveness in last 20 years, so-called learning organization has become the most successful corporation. Hence, the ability to learn faster than your competitors may have become the only sustainable competitive advantage.So far schools have had difficulties to adapt enterprises’ strategy to become ‘learning school’. Using the unique characteristics and resources of a school and its community is a way to create competitive curricula. Moreover, teachers are encouraged to employ information technology (multi-media software tools) to create and edit teaching materials.In the near future, school library will play the important role to help readers to learn how to learn by software and hardware tools. Teacher librarians will set up Information Communication Technology platform, organize some reading club to improve their reading behavior, hold information literacy seed teachers contest to upgrade readers information literacy capability and researching conference.for exchanging experience Meanwhle, developing catalogue and classification standards for digital materials by IEEE’s Draft Standard for Learning Object Metadata.provide informations for their readers from Internet and knowledge database, and learn the trend of fast advance of modern science and technology to make innovative 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreEmpirical · Other

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
Published2021
Admission routes1
Has abstractyes

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