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Record W3148273692 · doi:10.29173/iasl7800

School Library Learning Environment Assessment in International Schools

2021· article· en· W3148273692 on OpenAlexvenueno aff
Barbara Schultz‐Jones, Betty Turpin, Cynthia Ledbetter

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSchool libraryLearning environmentInternational schoolPerceptionMathematics educationPsychologyPsychosocialProcess (computing)PedagogyMedical educationComputer scienceLibrary scienceMedicine

Abstract

fetched live from OpenAlex

Evidence of how an assessment of the learning environment in an international school library equips the teacher librarian with valuable data that may challenge assumptions of how the learning environment is perceived by students is presented through disclosure of the full results of a two year research study examining the application of a learning environment paradigm to the lower school library setting in an international school in Germany. The process of completing a comparative examination of the relationships among student perceptions of their lower school library program with grade levels Third (ages 8 and 9), through Five (ages 10 and 11) is detailed. In addition, the assessment instruments will be discussed and presented. Use of these instruments in this study of the psychosocial aspects of the learning environment offers valuable ideas and techniques for teacher librarianconsideration and development.

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)0.004
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.004
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 head0.017
GPT teacher head0.306
Teacher spread0.288 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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