User Experience Research Techniques Facilitate Improvements for Access and Discovery Tools Managed by Technical Services Librarians
Notice bibliographique
Résumé
A Review of: Hill, K. (2020). Usability beyond the home page: Bringing usability into the technical services workflow. The Serials Librarian, 78 (1–4), 173–180. https://doi.org/10.1080/0361526X.2020.1702857 Objective – To demonstrate how user experience research techniques can be incorporated into technical services work. As proof of this concept, the author describes a case wherein a team of librarians, including one in a technical services role, deployed a user experience study to determine if students were able to successfully use LibGuides and the A-Z Database List to find subject-specific resources. The study also aimed to gauge the potential for several A-Z Database List interface redesign options. Design – A case study of user experience techniques applied to technical services projects, including a classic usability test of existing tools and an A/B/C comparison of potential interface redesigns. Setting – The library at the University of North Carolina Greensboro (UNCG), a public R2 university (doctoral university with high research activity). Subjects – Eleven student participants recruited through convenience sampling. Methods – The research team recruited study participants who were in the library at the time of the study, deselecting students from UNCG’s library school and those who were not currently affiliated with the university through an initial questionnaire. Eleven student participants were ultimately selected and led through a series of tasks related to finding subject-specific databases using the A-Z Database List and LibGuides. After the tasks for the A-Z Database List were completed, students were asked for their impression of two additional database list interfaces. Students were recorded throughout the tasks using the “talk aloud” method to provide researchers with insights on their thought processes and preferences. Following the study, researchers listened to the recordings, coding them as successful or incomplete and noting their observations for use in generalized findings. Main Results – Eight of eleven participants used the library’s main search box to locate a general resource for their major on the library’s homepage. When shown the A-Z Database List, ten out of eleven participants used the list to find a database for their major, while one used the link to “Research guides by subject” from that page. Comparisons of three A-Z Database List interfaces showed that most students preferred the Springshare Content Management System that allowed for filtering by subject area. When asked to find a research guide for their subject or major from the library’s homepage, nine out of eleven students clicked on the link labeled “Research guides by subject.” Starting from their subject guide, ten out of eleven could find a tab listing article databases. Nine participants noted that the number of databases listed on the guides was daunting. Conclusion – Results from the user experience study were used to support a redesign of the A-Z Database List using the Springshare Content Management System. The author regarded the experience as a whole as demonstrating how technical services librarians can become involved in user experience work and incorporate findings from usability studies into their management and design of tools that promote access and discoverability.
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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 | 0,047 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,009 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,006 |
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 ».