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Record W2136134241 · doi:10.5860/crl.76.7.934

Integrating Library Instruction into the Course Management System for a First-Year Engineering Class: An Evidence-Based Study Measuring the Effectiveness of Blended Learning on Students’ Information Literacy Levels

2015· article· en· W2136134241 on OpenAlex

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

VenueCollege & Research Libraries · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsInformation literacyLibrary instructionComputer scienceFlexibility (engineering)PaceWorkloadBlended learningTest (biology)Class (philosophy)Variety (cybernetics)Mathematics educationMultimediaMedical educationPsychologyWorld Wide WebEducational technologyMedicine

Abstract

fetched live from OpenAlex

This research examines students in a first-year engineering course who receive library instruction by using a newly developed online module and attending optional in-person tutorials. It aims to evaluate the outcomes of library information literacy instruction using this module combined with in-person help. Results show a significant improvement in information literacy skills from a pre-test to a post-test. Focus group and survey data indicate that most students preferred the self-paced learning style of the online module and that the content of the module helped them to conduct library research for the course. This study also considers best practices for online library instruction. A blended instruction approach provides students with the flexibility to learn from a variety of formats at their own pace and also reduces library staff workload, especially for a large course.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly 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 score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0020.049
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 head0.076
GPT teacher head0.359
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