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- All Subjects: Social Media
- Genre: Doctoral Dissertation
- Creators: Abbasi, Mohammad Ali, 1975-
- Status: Published
Description
This qualitative case study of 12, eighteen to twenty-four-year-olds from seven countries provided insight into the learning practices on an art-centered, social media platform. The study addressed two guiding questions; (a) what art related skills, knowledge, and dispositions do community members acquire using a social media platform? (b), What new literacy practices, e.g., the use of new technologies and an ethos of participation, collective intelligence, collaboration, dispersion of abundant resources, and sharing (Knobel & Lankshear, 2007), do members use in acquiring of art-related skills, concepts, knowledge, and dispositions? Data included interviews, online documents, artwork, screen capture of online content, threaded online discussions, and a questionnaire. Drawing on theory and research from both new literacies and art education, the study identified five practices related to learning in the visual arts: (a) practicing as professional artists; (b) engaging in discovery based search strategies for viewing and collecting member produced content; (c) learning by observational strategies; (d) giving constructive criticism and feedback; (e) making learning resources. The study presents suggestions for teachers interested in empowering instruction with new social media technologies.
ContributorsJones, Brian (Author) / Stokrocki, Mary (Thesis advisor) / Young, Bernard (Committee member) / Guzzetti, Barbara (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
ContributorsAbbasi, Mohammad Ali, 1975- (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2014