Social network analysis to influence career development

Higher education has traditionally been notorious for the inefficient use of data to improve the quality and the value of graduates in meeting market needs. The amount of data available to higher education sector surpasses their utilization opportunities, in the wake of big data field emergence. Managing, processing and analyzing these large volumes of data prompted the establishment of learning analytics (LA) field to improve learning processes (Ferguson 2012); and to bridge the gap between education and industry. Higher education graduates are expected to develop a set of qualities or dispositions necessary for building up their competency in embracing change, and making informed career decisions. Developing such a career-readiness capacity requires a sustained and progressive growth of professional habits. We propose a model that nurtures these dispositions, alongside the formal academic path. Community of practice (CoP) is an alternative informal way to achieve this aim (Jakovljevic et al. 2013). CoP fosters a new form of apprenticeship as students observe and emulate mentors, while engaging in a “learning to be” cycle to master the skill of a field. The practices and norms from established practitioners in Abstract 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 reciprocalweighted 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