abstract
- Numerous research articles are concerned with the issues surrounding the deployment of e-portfolios. Without proper mentorship, well-designed e-portfolios and stable systems the learner's experience is often negative. In this chapter, we review how to combine two Big data large-scale infrastructures, that is the JISC UK national experimental learning analytics (LA) and the Cedefop’s European Job Market Intelligence (JMI) infrastructure, to provide optimised and just in time advice. LA is a new data-driven field and is rich in methods and analytical approaches. LA focus is the optimisation of the learning environment by capturing and analysing the learners online digital traces. JMI digests vacancy data providing a broad overview of the job market including new and emerging skill demands. We look towards a future where we populate e-portfolios with authentic job market-related tasks providing transferable long-term markers of attainment. We populate through entity extraction running ensembles of Machine Learning algorithms across millions of job descriptions. We enhance the process with LA allowing us to approximate the skill level of the learner and select the tasks within the e-portfolio most appropriate for that learner relative to their local and temporal workplace demands.