Promoting the Library to Distance Education Students and Faculty Can Increase Use and Awareness, but Libraries Should Assess their Efforts
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».