Engineering Students and Professionals Report Different Levels of Information Literacy Needs and Challenges
Notice bibliographique
Résumé
A Review of: Phillips, M., Fosmire, M., Turner, L., Petersheim, K., & Lu, J. (2019). Comparing the information needs and experiences of undergraduate students and practicing engineers. The Journal of Academic Librarianship, 45(1), 39-49. https://doi.org/10.1016/j.acalib.2018.12.004 Abstract Objective – To compare the levels of information literacy, needs, and challenges of undergraduate engineering students with those of practising engineers. Design – Electronic survey. Setting – Large land grant university in the Midwestern United States and multiple locations of a global construction machinery manufacturing company (locations in Asia Pacific, Europe, North America). Subjects – Engineering undergraduates and full-time engineers. Methods – Two voluntary online surveys distributed to (a) students in two undergraduate engineering technology classes and one mechanical engineering class; and (b) to engineers in an online newsletter. None of the questions on the survey were mandatory. Because the call for practising engineers generated a low response rate, direct invitations were sent in batches of 100 to randomly selected engineers from a list provided by the human resources department of the company participating in the study. The surveys were similar but not identical and included multiple choice, Likert scale, and short answer questions. Data analysis included two-sided unpaired sample t-tests (quantitative data) and deductive and inductive content analysis (qualitative data). Main Results – There were 63 students and 134 professional engineers among the respondents. Survey response rates were relatively low (24.3% for students; approximately 4.5% for employees). Students rated themselves higher overall and significantly higher than did engineers on the questions “know where to look for information” (students M = 5.3; engineers M = 4.2) and “identifying the most needed information” (students M = 5.5; engineers M = 4.8) (mean values reported on a 7-point scale). Neither group rated themselves highly on “reflecting on how to improve their performance next time” or “having a highly effective structure for organizing information,” though engineers in North America rated themselves significantly higher than those in Asia Pacific on organizing information, knowing where to look for information, and using information to make decisions. Both students and engineers reported often using Google to find information. The library was mentioned by one-half of engineers and one-third of students. Engineers reported consulting with peers for information and making more use of propriety information from within their companies, while students reported using YouTube videos and online forums, as well as news and social media. More than half of students (57%) reported having enough access to information resources, while 67% of engineers felt that they lacked sufficient access. The most common frustration for both groups was locating the information (45% of student responses; 71% of engineer responses). Students reported more frustration with evaluating information (17%) compared to engineers (9%). Conclusion – Engineering students and professional engineers report differences in their levels of confidence in finding information and differences in the complexity of the information landscape. Engineering librarians at the university level can incorporate this knowledge into information literacy courses to help prepare undergraduates for industry. Corporate librarians can use this information to improve methods to support the needs of engineers at all levels of employment.
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 | 0,001 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,603 |
| Science ouverte | 0,000 | 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 ».