  This Work in Progress Research paper departs from the recent, turbulent
changes in global societies, forcing many citizens to re-skill themselves to
(re)gain employment. Learners therefore need to be equipped with skills to be
autonomous and strategic about their own skill development. Subsequently,
high-quality, on-line, personalized educational content and services are also
essential to serve this high demand for learning content. Open Educational
Resources (OERs) have high potential to contribute to the mitigation of these
problems, as they are available in a wide range of learning and occupational
contexts globally. However, their applicability has been limited, due to low
metadata quality and complex quality control. These issues resulted in a lack
of personalised OER functions, like recommendation and search. Therefore, we
suggest a novel, personalised OER recommendation method to match skill
development targets with open learning content. This is done by: 1) using an
OER quality prediction model based on metadata, OER properties, and content; 2)
supporting learners to set individual skill targets based on actual labour
market information, and 3) building a personalized OER recommender to help
learners to master their skill targets. Accordingly, we built a prototype
focusing on Data Science related jobs, and evaluated this prototype with 23
data scientists in different expertise levels. Pilot participants used our
prototype for at least 30 minutes and commented on each of the recommended
OERs. As a result, more than 400 recommendations were generated and 80.9% of
the recommendations were reported as useful.
