A Survey of Music Faculty in the United States Reveals Mixed Perspectives on YouTube and Library Resources
Notice bibliographique
Résumé
A Review of: Dougan, K. (2016). Music, YouTube, and academic libraries. Notes, 72(3), 491-508. https://doi.org/10.1353/not.2016.0009 Abstract Objective – To evaluate how music faculty members perceive and use video sharing sites like YouTube in teaching and research. Design – Survey Questionnaire. Setting – 197 music departments, colleges, schools, and conservatories in the United States. Subjects – 9,744 music faculty members. Methods – Schools were primarily selected based on National Association of Schools of Music (NASM) membership and the employment of a music librarian with a Music Library Association (MLA) membership. Out of faculty members contacted, 2,156 (22.5%) responded to the email survey. Participants were asked their rank and subspecialties. Closed-ended questions, ranked on scales of 1 to 5, evaluated perceptions of video sharing website use in classroom instruction and as assigned listening; permissibility as a cited source; quality, copyright, and metadata; use when items are commercially unavailable; use over library collections; comparative ease of use; and convenience. An open-ended question asked for additional thoughts or concerns on video sharing sites and music scholarship. The author partnered with the University of Illinois’ Applied Technology for Learning in the Arts and Sciences (ATLAS) survey office on the construction, distribution, and analysis of the survey data through SPSS. The open-ended question was coded for themes. Main Results – Key findings from closed-ended questions indicated faculty: used YouTube in the classroom (2.30 mean) more often than as assigned listening (2.08 mean); sometimes allowed YouTube as a cited source (2.35 mean); were concerned with the quality of YouTube recordings (3.58 mean) and accuracy of metadata (3.29 mean); and were more likely to use YouTube than library resources (2.62 mean), finding it easier to use (2.38 mean) and more convenient (1.83 mean). The author conducted further analysis of results for the nine most reported subdisciplines. Ethnomusicology and jazz faculty indicated a greater likelihood of using YouTube, while musicology and theory/composition faculty were more likely to use library resources than others. There was little significant difference among faculty responses based on performance subspecialities (e.g. voice, strings, etc.). Overall, open-ended faculty comments on streaming video sites were negative (19.3%), positive (19.3%), or a mixture of both (34.1%). Themes included: less use in faculty scholarship; a need to teach students how to effectively use YouTube for both finding and creating content; the value of YouTube as an audio vs. video source; concerns about quality, copyright, data, and reliability; and benefits like easy access and large amounts of content. Conclusion – Some faculty expressed concern that students did not use more library music resources or know how to locate quality resources. The study suggested librarians and faculty could collaborate on solutions to educate students. Librarians might offer instructional content on effective searching and evaluation of YouTube. Open-ended responses showed further exploration is needed to determine faculty expectations of library “discovery and delivery” (p. 505) and role as the purchaser of recordings. Conversations between librarians and faculty members may help clarify expectations and uncover ways to improve library resources and services to better meet evolving needs. Finally, the author recommended additional exploration is needed to evaluate YouTube’s impact on library collection development.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,050 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».