MétaCan
Menu
Retour à la cohorte
Enregistrement W2596246102 · doi:10.18260/1-2--5613

Teaming With Possibilities: Working Together To Engage With Engineering Faculty And Students

2020· article· en· W2596246102 sur OpenAlexaff
Jan Fransen, Jon Jeffryes

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueEngineering Education and Pedagogy
Établissements canadiensDominican University College
Organismes subventionnairesnon disponible
Mots-clésCurriculumLibrary scienceMedical educationSociologyPsychologyComputer sciencePedagogyMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Teaming with Possibilities: Working Together to Engage with Engineering Faculty and Students Last summer two of us moved into a shared office, starting our new positions as engineering librarians at the University of Minnesota. We support four engineering departments that total more than 100 full-time faculty, nearly 500 graduate students, and well over 1000 undergraduates. Janet Fransen has an undergraduate degree in engineering and was beginning her first library position after 20 years working in the technology sector. Jon Jeffryes has a background in the humanities and two years of professional library experience at the engineering library of another university. But neither of us had experience as a liaison librarian. When we started our jobs, we found ourselves sifting through the long list of duties in our job descriptions, contemplating just where to begin. As newly-minted librarians, we looked to the literature as well as our fellow liaisons for guidance. The job of a liaison librarian is busy and multi-faceted. The Reference and User Services Association division of the American Library Association includes expectations ranging from formal activities—"surveys of library users, faculty, staff and students to evaluate their satisfaction with library resources; regular meetings with faculty to ascertain planned curriculum developments and to identify new resources; communication of available materials and services; and establishment of a process by which library users can suggest purchases"—to the informal "participation in campus organizations and activities, monitoring campus media for activities and events that affect collections, and encouraging library use and support by nonusers."1 As if these static lists weren't enough, the liaison's role continues to evolve. As Frank, et al. posit, "the changing nature of scholarly communication and inquiry requires a more dynamic, communicative, and customized approach."2 As the needs of our users change with the times, the trend in liaison librarianship is a move toward more time-intensive, personalized services. With such a wide variety of activities ahead of us and limited hours in the day to test all possible methods, we decided to make the most of our differing strengths and experiences and formed a team approach to meeting the information needs of our engineering audience. Both overachievers, we tend to want to do it all ourselves. But between the numbers and the quickly changing landscape, we saw that we could be more effective if we worked together. So together we’re reaching out to our users through instruction, scholarly communication, and—of course—marketing. Katzenbach and Smith's definition of team has helped us step back and learn from each other's experiences: "a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable."3 Echoing Baughman's findings at the University of Maryland, we hoped that our team would "bring together a broad range and mix of individuals' skills in a collective way to support problem solving."4 Marketing Before we started our jobs, our academic departments had, naturally, worked with other librarians. Our predecessors had formed relationships with their departments and particular faculty members. In some cases, we were able to pick up where they left off. But we've found

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,527
Score d'incertitude au seuil0,560

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,255
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetEngineering Education and PedagogyTravaux en français237 207