Undergraduate Use of Library Databases Decreases as Level of Study Progresses
Notice bibliographique
Résumé
A Review of: Mbabu, L.G., Bertram, A. B., & Varnum, K. (2013). Patterns of undergraduates’ use of scholarly databases in a large research university. Journal of Academic Librarianship, 39(2), 189-193. http://dx.doi.org/10.10.1016/j.acalib.2012.10.004 Abstract Objective – To investigate undergraduate students’ patterns of electronic database use to discover whether database use increases as undergraduate students progress into later stages of study with increasingly sophisticated information needs and demands. Design – User database authentication log analysis. Setting – A large research university in the Midwestern United States of America. Subjects – A total of 26,208 undergraduate students enrolled during the Fall 2009 academic semester. Methods – The researchers obtained logs of user-authenticated activity from the university’s databases. Logged data for each user included: the user’s action and details of that action (including database searches), the time of action, the user’s relationship to the university, the individual school in which the user was enrolled, and the user’s class standing. The data were analyzed to determine which proportion of undergraduate students accessed the library’s electronic databases. The study reports that the logged data accounted for 61% of all database activity, and the authors suggest the other 39% of use is likely from “non-undergraduate members of the research community within the [university’s] campus IP range” (192). Main Results – The study found that 10,897 (42%) of the subject population of undergraduate students accessed the library’s electronic databases. The study also compared database access by class standing, and found that freshman undergraduates had the highest proportion of database use, with 56% of enrolled freshman accessing the library’s databases. Sophomores had the second highest proportion of students accessing the databases at 40%; juniors and seniors had the lowest percentage of use, with 38% of enrolled students at each level accessing the library’s databases. The study also found that November was the peak of database search activity, accounting for 37% of database searches for the Fall 2009 semester. Database use varied by the schools or colleges in which students were enrolled, with the School of Nursing having the highest percentage of enrolled undergraduates using library databases (54%). The authors also report that the College of Literature, Science, and the Arts had the fourth highest proportion of users at 46%, representing 7,523 unique students, more than double the combined number of undergraduate users from all other programs. Since the College of Literature, Science, and the Arts accounts for more than 60% of the total undergraduate enrollment, the authors suggest that information literacy instruction targeted to these programs would have the greatest campus-wide impact. Conclusion – Although the library conducts a number of library instruction sessions with freshman students each Fall semester, the authors conclude that database use patterns suggest that the proportion of students who continue to use library databases decreases as level of study progresses. This finding does not support the study’s hypothesis that database use increases as students advance through their undergraduate studies.
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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 | 0,004 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,007 |
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 ».