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Social network analysis to influence career development
UAE University, Al Ain, United Arab Emirates.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-7312-9089
2017 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145Article in journal (Refereed) Epub ahead of print
Abstract [en]

Social network analysis techniques have shown a potential for influencing gradu-ates to meet industry needs. In this paper, we propose a social-web driven solutions to bridge formal education and industry needs. The proposed career development frame-work utilizes social network analytics, influence diffusion algorithms and persuasive technology models along three phases: (1) career readiness to measure and visualize the general cognitive dispositions required for a successful career in the 21st Century, (2) career prediction to persuade future graduates into a desired career path by clustering learners whose career prospects are deemed similar, into a community of practice; and (3) career development to drive growth within a social network structure where social network analytics and persuasive techniques are applied to incite the adoption of desired career behaviors. The process starts by discovering behavioral features to create a cognitive profile and diagnose individual deficiencies. Then, we develop a fuzzy clustering algorithm that predicts similar patterns with controlled constraint-violations to construct a social structure for collaborative cognitive attainment. This social framework facilitates the deployment of novel influence diffusion approaches, whereby we propose a reciprocal-weighted similarity function and a triadic clo-sure approach. In doing so, we investigate contemporary social network analytics to maximize influence diffusion across a synthesized social network. The outcome of this social computing approach leads to a persuasive model that supports behavioral changes and developments. The performance results obtained from both analytical and experi-mental evaluations validate our data-driven strategy for persuasive behavioral change.

Place, publisher, year, edition, pages
Springer, 2017.
Keyword [en]
Social web, Community of practice, Big data, Learning analytics, Computational science, Fuzzy logic
National Category
Computer and Information Science
Research subject
Humanities and Social sciences; Technology; Distributed Real-Time Systems
Identifiers
URN: urn:nbn:se:his:diva-13398DOI: 10.1007/s12652-017-0457-9OAI: oai:DiVA.org:his-13398DiVA: diva2:1075358
Available from: 2017-02-18 Created: 2017-02-18 Last updated: 2017-05-23

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Atif, Yacine
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf