Hands on Digital Information Literacy Training from Peers is Preferred by Public Service Library Staff
Notice bibliographique
Résumé
A Review of:
 Robertson, R. (2014). Reframing ourselves: Digital information literacy skills of frontline public library staff. New Zealand Library and Information Management Journal, 53(3). doi:10.1080/00048623.2011.10722203
 
 Abstract
 
 Objective – To explore how and where public library employees acquire digital information literacy (DIL) skills.
 
 Design – Qualitative study using semi-structured interviews.
 
 Setting – Two public libraries in New Zealand.
 
 Subjects – Nine front line public library staff members.
 
 Methods – A convenience sample of nine library employees was interviewed about their existing DIL skills, how and where they learned them, any barriers to this learning, and how they defined DIL in others. Interviewees ranged in age from 40 to 64 and included both those new to libraries and those with over 25 years in the profession. The interview transcripts were analyzed for key themes and placed in the theoretical framework of Kolb’s experiential learning cycle (Robertson, 2014).
 
 Main Results – Five participants described their own DIL skills as average or below average. The remaining participants classified their skills as above average. Participants recounted acquiring DIL skills in the course of their work through formal workplace training sessions, peer support, or individual exploration; through personal exploration of tools on their own time; or through a mix of work and personal learning opportunities. The barriers they identified to their learning included insufficient time to train and practice the skills learned and the lack of access to relevant technologies. Participants noted problems such as accessing key hardware and insufficient Internet connectivity at work because of issues with organizational infrastructure and at home due to personal financial constraints. Participants largely preferred informal hands-on training by peers to formal training sessions, which were described by some as too general or held too far in advance of the implementation of new technology. The data suggested participants largely fell into Kolb’s accommodating or diverging learning styles because of their preference for “concrete experience” (Robertson, 2014).
 
 Conclusion – Libraries may improve staff acquisition of DIL skills by increasing hands on learning opportunities and providing dedicated time to review and practice skills learned. Other suggestions included identifying potential digital peer mentors among staff and providing them with the necessary resources (time, money, and a defined role) to support their colleagues, breaking training into parts allowing time for practice, creating training plans tied to performance evaluation, and using incentives to encourage staff to participate in self-directed training.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,008 | 0,762 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».