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Record W2146144155 · doi:10.18438/b86k74

Undergraduate Use of Library Databases Decreases as Level of Study Progresses

2014· article· en· W2146144155 on OpenAlexvenueno aff
Kimberly Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseSubject (documents)PopulationClass (philosophy)Computer scienceAction (physics)World Wide WebMedicine

Abstract

fetched live from OpenAlex

A Review of:
 Mbabu, L.G., Bertram, A. B., & Varnum, K. (2013). Patterns of undergraduates’ use of scholarly databases in a large research university. Journal of Academic Librarianship, 39(2), 189-193. http://dx.doi.org/10.10.1016/j.acalib.2012.10.004
 
 Abstract
 
 Objective – To investigate undergraduate students’ patterns of electronic database use to discover whether database use increases as undergraduate students progress into later stages of study with increasingly sophisticated information needs and demands.
 
 Design – User database authentication log analysis.
 
 Setting – A large research university in the Midwestern United States of America.
 
 Subjects – A total of 26,208 undergraduate students enrolled during the Fall 2009 academic semester.
 
 Methods – The researchers obtained logs of user-authenticated activity from the university’s databases. Logged data for each user included: the user’s action and details of that action (including database searches), the time of action, the user’s relationship to the university, the individual school in which the user was enrolled, and the user’s class standing. The data were analyzed to determine which proportion of undergraduate students accessed the library’s electronic databases. The study reports that the logged data accounted for 61% of all database activity, and the authors suggest the other 39% of use is likely from “non-undergraduate members of the research community within the [university’s] campus IP range” (192).
 
 Main Results – The study found that 10,897 (42%) of the subject population of undergraduate students accessed the library’s electronic databases. The study also compared database access by class standing, and found that freshman undergraduates had the highest proportion of database use, with 56% of enrolled freshman accessing the library’s databases. Sophomores had the second highest proportion of students accessing the databases at 40%; juniors and seniors had the lowest percentage of use, with 38% of enrolled students at each level accessing the library’s databases. The study also found that November was the peak of database search activity, accounting for 37% of database searches for the Fall 2009 semester. Database use varied by the schools or colleges in which students were enrolled, with the School of Nursing having the highest percentage of enrolled undergraduates using library databases (54%). The authors also report that the College of Literature, Science, and the Arts had the fourth highest proportion of users at 46%, representing 7,523 unique students, more than double the combined number of undergraduate users from all other programs. Since the College of Literature, Science, and the Arts accounts for more than 60% of the total undergraduate enrollment, the authors suggest that information literacy instruction targeted to these programs would have the greatest campus-wide impact.
 
 Conclusion – Although the library conducts a number of library instruction sessions with freshman students each Fall semester, the authors conclude that database use patterns suggest that the proportion of students who continue to use library databases decreases as level of study progresses. This finding does not support the study’s hypothesis that database use increases as students advance through their undergraduate studies.

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 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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.771
Open science0.0000.000
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 head0.096
GPT teacher head0.348
Teacher spread0.252 · 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 teacher head, not a consensus.

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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