Public Youth Librarians Use Technology in Ways that Align with Connected Learning Principles but Face Challenges with Implementation
Notice bibliographique
Résumé
A Review of: Subramaniam, M., Scaff, L., Kawas, S., Hoffman, K. M., & Davis, K. (2018). Using technology to support equity and inclusion in youth library programming: Current practices and future opportunities. The Library Quarterly, 88(4), 315–331. https://doi.org/10.1086/699267 Abstract Objective – To understand how public youth librarians use technology in their programming and what challenges and opportunities they face incorporating connected learning into their programming. Design – Qualitative study Setting – Phone calls and three library conferences (the Young Adult Library Services Association Symposium, the American Library Association Midwinter Meeting, and the Maryland/Delaware Library Association Conference) in the United States. Phone calls; in-person interviews; focus groups at the Young Adult Library Services Association (YALSA) Symposium, the American Library Association (ALA) Midwinter Meeting, and the Maryland/Delaware Library Association Conference. Subjects – A total of 92 youth-serving librarians and library staff in rural, urban, and suburban public libraries across the United States. Methods – Subjects were recruited via social media, partner librarians, the project website, an association e-newsletter, and printed materials. The researchers conducted 66 semi-structured interviews between December 2015 and May 2016 and 3 focus groups between November 2015 and May 2016. The transcripts of the interviews and focus groups were coded using a thematic analysis approach informed by a connected learning framework. Main Results – A total of 98% (65) of interview participants said they use technology in their youth programming; 69% (18) of focus group participants mentioned using technology in their youth programming. Many youth-serving librarians use technology in ways that align with connected learning. Youth-serving library workers are successful in finding community partners to help plan technology-enabled programming, they strive to develop connected learning programming based on the interests of their youth patrons, and they often take on the role of “media mentor” by exploring technology collaboratively with their patrons. Youth-serving library workers face several challenges in implementing connected learning. These include difficulties with openly networked infrastructures, struggling to create learning environments that align with the hanging out, messing around, and geeking out (HOMAGO) stages of connected learning, and lack of confidence and experience in mentoring youth patrons on how to use technology. Conclusion – The authors recommend that library administrators improve access to openly networked technology both within and outside the library, and loosen overly-restrictive social media policies to give youth-serving library workers more flexibility and control. They also recommend that library administrators implement more training for library staff in skills relating to connected learning. The authors are creating a professional development toolkit to help public youth library workers to incorporate digital media and connected learning into their work with young patrons.
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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 | 0,007 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,014 | 0,015 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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 ».