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Record W3137356233 · doi:10.3138/jelis.61.4.2019-0056

How Master of Library Studies Students Learn to Search for Information: A Pilot Study

2020· article· en· W3137356233 on OpenAlex
Lindsay McNiff, Lauren Hays

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Education for Library and Information Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsScholarshipInformation seekingInformation literacyPopulationMathematics educationPsychologyInformation needsComputer scienceLibrary sciencePedagogySociology

Abstract

fetched live from OpenAlex

Master of Library and Information Studies (MLIS) students represent a population for whom literature searching is a core practice and a learning outcome for an entry-level course on information searching. How LIS students learn to find information, though, is not completely clear. Many studies have explored undergraduate searching behavior, but few recent studies have investigated the search behaviors of MLIS students. The purpose of this Scholarship of Teaching and Learning study was to explore the following research questions: (1) How do MLIS students describe learning to search?; (2) What works in helping MLIS students see themselves as better searchers of information?; and (3) What works in helping MLIS students become better searchers of information? Participants articulated that course sequence was important in their development of searching skills, that demonstrable skills and engagement with research improved their view of themselves as searchers, and that course structure, content, and active learning were important factors in their improvement.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.318
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.094
GPT teacher head0.377
Teacher spread0.283 · 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