Use of Diverse Online Resources amongst Politically Active University Students Fosters Civic Knowledge Integration
Notice bibliographique
Résumé
A Review of:
 Soe, Y. (2018) Understanding politics more thoroughly: How highly engaged young citizens use the Internet for civic knowledge integration. First Monday, 23(6), 1-17. http://doi.org/10.5210/fm.v23i6.7923
 Abstract
 Objective – To examine the process by which university students with a high interest in politics and public affairs incorporate new information into their understanding of politics and public affairs, a process referred to as civic knowledge integration.
 Design – This study utilized a qualitative research design that consisted of focus group interviews and essay questions.
 Setting – Two large four-year Midwestern public universities and two four-year East coast private universities in the United States of America in 2008 and 2010.
 Subjects – A total of 65 undergraduate and graduate (masters) students participated in the focus group interviews and answered essay questions by e-mail.
 Methods – In 2008, the researcher conducted 11 focus groups consisting of 5 to 7 participants per group. In 2010, additional data were collected from students at another large four-year Midwestern public university who responded by e-mail to essay questions that were adapted from those used in the focus groups. Recruitment of participants was achieved by contacting professors of media and political science at the universities and targeting students with interest in media, politics, and public affairs, and who were politically active. Course credit or a small monetary incentive was offered to students as compensation. Data resulting from the focus group and essay responses were combined and imported into the QDA Miner software. Data analysis, which used some techniques of grounded theory, was conducted in two phases. In the first phase, 120 analyzable subsets were identified, and open coding of 36 subsets was performed to determine themes. These themes were then modified and renamed using an axial and selective coding process. Examples of resulting topics included collaborative layering of ideas, comparison of differing viewpoints, and monitorial scanning. The second phase involved coding of the 120 subsets, and 65 subgroups that focused on civic knowledge integration were identified. Ultimately, open, thematic coding of the 65 subsets was performed to identify comments that contained the most common themes.
 Main Results – An analysis of the data revealed that participants used the resulting themes as self-guided learning strategies when searching the Internet for civic knowledge integration, the process by which university students with a high interest in politics and public affairs incorporate new information into their understanding of these areas. One of the strategies used was a two-step process of monitorial scanning and opinion sampling. Monitorial scanning involves the careful selection of search engines in order to scan the news and determine their potential levels of interest, and the use of online encyclopedias such as Wikipedia to locate background information and other details. Opinion sampling involves the process of sorting the sources found, such as blogs and candidates’ web pages. Another strategy used was verification (cross-checking), which consisted of checking multiple sources to find more information on a particular news item or news show, such as those watched on CNN.com. Comparison of differing and opposing viewpoints was another strategy used, that involved the comparing of information about political candidates' perspectives or views to justify their own opinions. Finally, collaborative layering of ideas was a strategy that involved participation in online forums, such as Facebook. This strategy provided participants with the opportunity to express their thoughts and opinions globally, and to contribute to a change in a set practice. 
 Conclusion – Through the use of strategies for self-guided learning, participants were able to add new information to their knowledge base and to develop new points of view. These students developed advanced search strategies and took pleasure in finding opposing perspectives, and as a result, enhanced their critical thinking skills. The conclusions also increased general knowledge of why young people used specific online platforms, information resources, or social media sites to enhance their understanding of politics and public affairs. These findings may also challenge media and political science to investigate the long-term effects of self-guided learning strategies for civic knowledge integration practiced by some young people.
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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 | 0,000 | 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 | 0,000 | 0,110 |
| 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 ».