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Record W3127661189 · doi:10.29173/iasl7619

Information Needs and Information Seeking Behaviours of Elementary and Middle School Social Studies and Cultural Heritage Teachers in Taiwan

2021· article· en· W3127661189 on OpenAlexvenueno aff
Shan-Ju L. Chang

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsInformation seekingInformation needsCultural heritageData collectionPsychologyQualitative researchQualitative propertySocial needsInformation seeking behaviorInformation behaviorMathematics educationMedical educationSociologyComputer scienceSocial scienceWorld Wide WebPolitical scienceLibrary scienceMedicine

Abstract

fetched live from OpenAlex

Employing both qualitative and quantitative approaches, this study seeks to systematically analyze the information needs, seeking, and use behaviors of elementary and middle school social studies and cultural heritage teachers in Taiwan, and to relate the findings to digital library system design. In-depth interviews and questionnaire surveys were used for data collection. Data was analyzed qualitatively and statistically in order to answer a number of questions regarding: the information needs for teaching social studies courses, information seeking and collecting behaviors, difficulties encountered while searching for information for teaching, factors influencing information needs and seeking behaviors, and teacher expectations of educational websites covering related subjects. Our results suggest practical applications and implications for education policy makers and library system designers.

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)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.078
GPT teacher head0.350
Teacher spread0.271 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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