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Record W2167387835 · doi:10.1002/meet.14504701309

Factors affecting student learning outcomes of information literacy instruction

2010· article· en· W2167387835 on OpenAlexafffundabout
Brian Detlor, Heidi Julien, Alexander Serenko, Lorne D. Booker

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsLakehead UniversityUniversity of AlbertaMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDemographicsAffect (linguistics)Information literacyPsychologyMedical educationPerceptionLiteracyQuality (philosophy)Mathematics educationPedagogyMedicineDemographySociology

Abstract

fetched live from OpenAlex

Abstract This poster reports results of a survey conducted recently at a Canadian business school concerning factors affecting student learning outcomes of information literacy instruction (ILI). Specifically, the effects of demographics, learning environment factors, and information literacy components on behavioral, psychological, and benefit outcomes of ILI are examined. Results test qualitative findings reported in a paper by the authors at last year's ASIST Annual Meeting (Julien et al., ), and identify the salient factors surrounding the delivery of ILI that affect student learning outcomes. Specifically, results show that greater amounts of active instruction, more senior students, and more positive perceptions of the quality of and satisfaction with ILI, all yield improved student learning outcomes.

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)0.016
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.300
Teacher spread0.291 · 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

Citations7
Published2010
Admission routes3
Has abstractyes

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