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Record W2996599260 · doi:10.18438/eblip29622

Promoting the Library to Distance Education Students and Faculty Can Increase Use and Awareness, but Libraries Should Assess their Efforts

2019· article· en· W2996599260 on OpenAlexaffvenue
Judith Logan

Bibliographic record

VenueEvidence Based Library and Information Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDistance educationDescriptive statisticsLikert scalePromotion (chess)Medical educationLibrary sciencePsychologySociologyMedicineMathematics educationComputer sciencePolitical scienceMathematics

Abstract

fetched live from OpenAlex

A Review of:
 Bonella, L., Pitts, J., & Coleman, J. (2017). How do we market to distance populations, and does it work?: Results from a longitudinal study and a survey of the profession. Journal of Library Administration, 57(1), 69–86. https://doi.org/10.1080/01930826.2016.1202720 
 Abstract
 Objective – To determine if library promotion efforts targeted at distance education students and instructors were successful and in line with similar activities at other institutions
 Design – Mixed: longitudinal and survey questionnaire
 Setting – Large publicly-funded, doctoral-granting university in the midwestern United States
 Subjects – 494 distance education students and instructors in 2014 compared to 544 in 2011 and “more than 300” (Bonella, Pitts, & Coleman, 2017, p. 77) professionals at American academic libraries.
 Methods – In the longitudinal study, the researchers invited all distance education students and instructors who were active in the 2010-2011 academic year (n = 8,793) and the spring 2014 semester (n = 4,922) to complete an online questionnaire about their awareness and use of library’s services. Questions were formatted as multiple choice or Likert scale with optional qualitative comments. The researchers used descriptive statistics to compare the responses.
 Then, the researchers invited library professionals via relevant distance-education and academic library listservs to complete an online questionnaire about how distance education is supported, promoted, and assessed. Free text questions comprised the majority of the questionnaire. The researchers categorized these and summarized them textually. The researchers used descriptive statistics to collate the responses to the multiple-choice questions.
 Main results – The researchers observed an increase in awareness of all the library services about which they asked undergraduates. Off campus access to databases (92%, n = 55), an online course in the learning management system (78%, n = 47), and online help pages (71%, n = 43) had the highest awareness in 2014 as compared to 2011 when off campus access to databases (73%, n = 74), research guides (43%, n = 44), and online help pages (42%, n = 43) were the top three most visible items. Fewer undergraduates said they do not use the library at all between 2011 (54%, n = 56) and 2014 (30%, n = 18).
 More graduate students reported that they were very satisfied with the library in 2014 (45%, n = 12) than in 2011 (27%, n = 10).
 Faculty members were more aware of library services, especially research guides, which had 79% awareness in 2014 (n = 56) up from 60% (n = 55) in 2011. Almost half (46%) of faculty member respondents had recommended them to students in 2014 as compared to 27% in 2011.
 The library professionals who responded indicated that their institutions did not evaluate the success of distance educators and students’ awareness of the library’s services and resources (54%, n = 97) nor the success of any promotional campaigns they may have undertaken (84%, n = 151). Both the respondents (37%, n = 54) and the authors recommended partnering with faculty members as a best practice to promote the library.
 Conclusion – More libraries should be marketing specifically and regularly to distance education students by leveraging existing communication and organizational structures. Assessing these efforts is important to understanding their effectiveness.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0060.376
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 head0.056
GPT teacher head0.343
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2019
Admission routes2
Has abstractyes

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