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Social network analysis to influence career development
UAE University, Al Ain, United Arab Emirates.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID-id: 0000-0002-7312-9089
2018 (engelsk)Inngår i: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 9, nr 3, s. 601-616Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
Springer, 2018. Vol. 9, nr 3, s. 601-616
Emneord [en]
Social web, Community of practice, Big data, Learning analytics, Computational science, Fuzzy logic
HSV kategori
Forskningsprogram
Distribuerade realtidssystem (DRTS); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:his:diva-13398DOI: 10.1007/s12652-017-0457-9ISI: 000434911600011Scopus ID: 2-s2.0-85048261802OAI: oai:DiVA.org:his-13398DiVA, id: diva2:1075358
Tilgjengelig fra: 2017-02-18 Laget: 2017-02-18 Sist oppdatert: 2018-07-05bibliografisk kontrollert

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