Social network analysis to influence career development
2017 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145Article in journal (Refereed) Epub ahead of print
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
Social web, Community of practice, Big data, Learning analytics, Computational science, Fuzzy logic
Computer and Information Science
Research subject Humanities and Social sciences; Technology
IdentifiersURN: urn:nbn:se:his:diva-13398DOI: 10.1007/s12652-017-0457-9OAI: oai:DiVA.org:his-13398DiVA: diva2:1075358