Towards a Standardized Machine-Readable Metadata Format for MOOC Platforms

Abstract

Nowadays, there are many online courses like MOOCs (Massive Open Online Courses) available from different providers (e.g. edX, Coursera, openHPI, OpenWHO, iMOOX). To support learners, aggregators like Class Central or MOOChub were established. These aggregators hold catalogs with the offerings of the providers making them a central entry point for the users. Such catalogs are based on metadata, which needs to be formatted in a proper way. This metadata can then be used for filtering courses and recommendation engines also. With more and more emerging AI-based recommendation services for learning opportunities and learning path assistants, the need for well-maintained and meaningful metadata is growing massively. In this paper, we report on our research about different systems for categorizing the fields of study, topic, or subject, which can be used to enhance existing metadata formats. An overview of field of study categorization systems of different entities (e.g. international, national, and private organizations) is given. The systems are compared regarding their usefulness in metadata formats for the description of courses. The results are utilized to refine our own metadata format and represent a further step towards a standardized metadata format for courses and automatically generated metadata.