Big learning data analytics support for engineering career readiness

21st Century education develops professional competencies such as lifelong learning, critical thinking and creativity. Learning analytics (LA) and Big Data approaches are emerging to discover readiness patterns of higher education learners towards embracing these competences. To supplement formal education into building these career-oriented habits, a portal application is proposed by feeding data streams gathered from learners throughout their higher-education lifetime, into an analytics engine that reveals levels of professional competencies achievement and tells learners how to increase their career readiness. Big Data techniques are used to gather and cluster data streams, whereas LA techniques examine these data to derive levels of career readiness, which guide higher education learners into a successful career path. The proposed methodology starts by defining and validating standard career disposition criteria inspired from education psychology literature. These intangible disposition indicators and other career-oriented data are stored in a data warehouse for further aggregation and analysis, to evaluate similarities among career patterns and to cluster individuals whose career prospects are deemed similar, into a community of practice (CoP). We synthesize this novel online social structure as a result of the proposed portal processes to augment formal education with a virtual learning environment. This informal classroom-like structure is supervised by a professional mentor to bridge diverse viewpoints and instill a joint effort to leverage future workforce developments. CoP also provides indicators and means to intervene in order to positively affect career readiness.