Open Educational Resources (OER) are digital learning materials available for free on the Internet which provide the user the rights to reuse, adapt and redistribute. For teachers, OER provide more opportunities to diversify their lectures by reusing available OER. An exact number of OER available worldwide cannot be given, but estimations run into many millions. To better find these resources, in many cases metadata are added. However, adding metadata is a tedious task, best done by a professional (e.g. a librarian). These professionals are not always available. Also, standards used for metadata (e.g. IEEE LOM) do not provide topic specific information a teacher uses when searching for OER. Therefore, low findability of the right resources for specific contexts is experienced as a barrier for teachers, thereby hindering adoption of OER.
To overcome these barriers, machine learning techniques are developed to automatically add metadata to resources. In this talk an overview will be given of experiments in this area and the first results of these experiments.