Larger, Higher-level Academic Institutions in the US Do Not Necessarily Have Better-resourced Library Web Teams.
Notice bibliographique
Résumé
A Review of:
 Connell, Ruth Sara. "Survey of Web Developers in Academic Libraries." The Journal of Academic Librarianship 34.2 (2008): 121-29.
 
 Objective – To discover how library Web teams’ staffing, backgrounds, tools, and professional development differ among various types of academic libraries.
 
 Design – Survey.
 
 Setting – Academic libraries in the United States.
 
 Subjects – Academic library Web team members.
 
 Methods – A systematic sample of every twelfth institution on The Carnegie Classification of Institutions of Higher Education list was used to establish a sample group. A Web search was carried out to identify each institution’s library Web site and contact information for the Web site designer or most appropriate alternative person. Institutions were excluded from the sample if they had no Web site at all, had no library Web site, had a Web site that did not mention a library, or had a Spanish-language Web site.
 
 In September 2006 an e-mail was sent to the contact for each institution in the sample group asking them to participate in an online survey. A follow up e-mail was sent two weeks later and the survey closed after one month. The survey respondents were asked to identify their institutions so that analysis of the results in relation to the size and type of institution could be carried out. The researchers used a simplified version of the Carnegie classification to sort the responding institutions into five main groups.
 
 Main Results – The systematic sample consisted of 288 institutions (sample size 6.5%).
 The profile of the responding institutions was as follows: associate’s colleges (35.5%), baccalaureate colleges (18.2%), master’s colleges and universities (20.9%), doctorate-granting universities (9%) and special focus institutions (15.5%). A total of 110 institutions completed the survey, yielding a response rate of 38.19%, although not all respondents answered all the survey questions. The final sample of 110 was 2.5% of the total 4384 institutions on the Carnegie list.
 
 Seventy-one per cent of institutions with multiple libraries shared Web teams, with two-year colleges more likely (91.7%) to share a Web team than four-year or above institutions (60.9%). The majority of responding institutions (94.4%) used in-house library Web site design, with only 5.6% of respondents outsourcing this task. Nearly half (49%) of respondents indicated that library Web design was done by one person and even the larger libraries did not necessarily have larger Web teams. 
 
 Very few Web team members (4.9%) had Web design as their primary role; the majority (83.5%) indicated that it was just one component of their job. Web team members from master’s- and doctorate-granting institutions were more likely to have taught themselves Web design, while those from associate, baccalaureate and special focus colleges were more likely to have taken Web design courses. For all respondents, the most commonly listed quality for selection to the Web team was an interest in Web design and the most valued skill for library Web designers was the ability to organise information effectively. Knowledge of Web authoring software and basic HTML coding were the most commonly listed knowledge requirements for Web team members. A significant number of respondents indicated that they or other Web team members did not have access to Web authoring (36.9%) and image editing (52%) software. 
 
 Generally (except for two very large institutions), the larger institutions were more likely to use database-driven systems for their library Web sites and the smaller institutions were more likely to use content management systems. Associate’s and special focus colleges were less likely than other types of institutions to use either database driven or content management systems. Associate’s institutions were more likely to achieve ADA (Americans with Disabilities Act) Web accessibility compliance. Only 48.6% of respondents utilised usability testing during Web site design.
 
 Conclusion – The author expected that institutions providing higher levels of education would have better-resourced Web design and training, but the results of the survey did not support this expectation. One reason why associate’s colleges performed better than other institutions in some areas of Web design may be that these colleges tend to offer more Web design and computer technology courses than baccalaureate, master’s and doctorate-granting institutions.
 
 Web site design and testing attracted fewer resources than might be expected in academic libraries. Across all types of institutions, Web design tended to be the responsibility of a small team or one person, with most Web designers having other responsibilities apart from the library’s Web site. Just over half of the institutions surveyed did not implement usability testing of their library Web sites.
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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 | 0,000 |
| 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 | 0,001 | 0,446 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; 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 ».