Leaving the Library: How We Improved Information Literacy by Joining Our User Communities
Notice bibliographique
Résumé
Presentation at the Workshop for Instruction in Library Use (WILU) held in Winnipeg, MB, Canada, May 22-24, 2019. How can we develop a better understanding of the goals of our user communities and what they’re trying to accomplish? What can we do to ensure that our information literacy goals and initiatives align with what our students need to learn? How do we demonstrate our value and expertise to our user communities? One strategy is to disrupt where we practice librarianship. By practicing librarianship solely in the library, our practice is shaped mainly by the library. Moving out of the library and inhabiting the space where our students and faculty work gives librarians opportunities to engage with and develop strong working relationships with our program faculty and stakeholders. We can then use these strong working relationships to better learn about the culture, goals, and needs of our user communities and align our information literacy goals and initiatives with them. By focusing our information literacy initiatives to what will have the biggest impacts on our user communities, and through partnerships with faculty and campus stakeholders, we become seen as a valuable partner in problem-solving and meeting their goals. Our practice of librarianship becomes informed by and integrated into our user communities. This presentation describes the process of getting librarians out of the library and engaged with their user communities at the University of Michigan-Dearborn and the University of Michigan, Ross School of Business. We also discuss strategies that librarians used to build relationships with faculty and other stakeholders in their program areas as well as those used to learn about the program’s culture, goals, and needs. Librarians were able to leverage this into integrated information literacy initiatives tailored to these goals and needs and developed in collaboration with partners in their user communities, which had a greater impact on desired student outcomes. This increased the perceived importance of information literacy learning and awareness of librarian expertise among program faculty and stakeholders, who also found it easier to collaborate with their librarians. It also became easier and more motivating for students to consult their librarian and use library resources. By moving into the spaces where our students and faculty work and learn, we were able to develop high-impact information literacy goals and initiatives aligned with those of our user communities and demonstrate our value.
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 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 | 0,000 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,043 |
| Science ouverte | 0,001 | 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 ».