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Record W3133994757 · doi:10.29173/iasl7453

Inquiry learning

2021· article· en· W3133994757 on OpenAlexvenueno aff
Adriana Bogliolo Sirihal Duarte, Bernadete dos Santos Campello

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsInformation literacyProcess (computing)PsychologyMathematics educationPedagogyComputer science

Abstract

fetched live from OpenAlex

Inquiry learning is a concept familiar to Brazilian librarians, who have been expressing concern about their contribution in innovating the learning process. It is therefore necessary that future librarians experience this learning strategy during their education. This study aimed to investigate: 1) how library students exposed repeatedly to strategies of inquiry learning react; 2) the difficulties they encounter in the process; 3) and what types of learning they acquire. Data were collected through in depth interviews with undergraduate library students taking an Information Literacy Course. Data analysis was based on Kuhlthau’s ISP model (2004) and in the five types of learning (Kuhlthau; Maniotes & Caspari, 2012) Results, that cannot be generalized, show that students reacted positively to the strategy, although they reported several difficulties. In conclusion the repetition of the inquiry learning process became important for students to feel more secure and confident and for their difficulties to be minimized. On the whole the acquisition of the five types of learning was observed.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0100.007
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0580.021

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 head0.036
GPT teacher head0.263
Teacher spread0.227 · 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 designNot applicable
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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