Promoting the Library to Distance Education Students and Faculty Can Increase Use and Awareness, but Libraries Should Assess their Efforts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".