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Social network analysis to influence career development

Khousa, Eman ; Atif, Yacine

Journal of Ambient Intelligence and Humanized Computing, 2018, Vol.9(3), pp.601-616 [Tạp chí có phản biện]

ISSN: 1868-5137 ; E-ISSN: 1868-5145 ; DOI: 10.1007/s12652-017-0457-9

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  • Nhan đề:
    Social network analysis to influence career development
  • Tác giả: Khousa, Eman ; Atif, Yacine
  • Chủ đề: Social web ; Community of practice ; Big data ; Learning analytics ; Computational science ; Fuzzy logic
  • Là 1 phần của: Journal of Ambient Intelligence and Humanized Computing, 2018, Vol.9(3), pp.601-616
  • Mô tả: Social network analysis techniques have shown a potential for influencing graduates 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 framework 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 closure 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 experimental evaluations validate our data-driven strategy for persuasive behavioral change.
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 1868-5137 ; E-ISSN: 1868-5145 ; DOI: 10.1007/s12652-017-0457-9

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