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Record W2752635106 · doi:10.29173/iasl7186

“It’s like stickers in your brain”: Using the Guided Inquiry Process to Support Lifelong Learning Skills in an Australian School Library

2016· article· en· W2752635106 on OpenAlexvenueno aff
Kasey Garrison, Lee A. Fitzgerald

Bibliographic record

VenueIASL Annual Conference Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Process (computing)CurriculumAutonomyPsychologyPedagogyLifelong learningMathematics educationComputer sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

The Guided Inquiry Design process (GID) is a model of information seeking behaviour emphasising elements of autonomy and reflection throughout students’ research process and based on Kuhlthau’s (1989a; 2004) Information Search Process (ISP). GID is timely in the Australian context as a way to support the new Australian curriculum emphasising inquiry learning but omitting a practical framework for implementing it. This study sought to investigate the experience of students engaged in two GI research projects in Year 7 History and Geography at an independent girls’ school in an Australian urban area. Analysis of the data indicates rich and diverse interpretations of the GID process across participants. Freddo’s comment “It’s like stickers in your brain,” the title of this paper, highlights the memorability of the stages of the GI process. The girls also noted rewarding responses through their learning of the content and skills and “had fun” in this project.

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)0.020
metaresearch head score (Gemma)0.032
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 score0.021
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.013
Scholarly communication0.0080.006
Open science0.0030.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.076
GPT teacher head0.380
Teacher spread0.303 · 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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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