Learning Object Metadata Workflows for Description, Findability and Reusability Improvement

Abstract

With the increase of online education, a good description of learning resources has become vital for educational resource sharing and reuse. Resource description has been under the spotlight in recent years. Educational platforms can benefit from good resource organisation and description, thereby providing a higher quality of services and attracting more learners to use their systems. Furthermore, well-described resources with metadata, promote content sharing and re-use.
This work starts with an extensive literature research on metadata generation techniques and breaks the findings down to metadata types. A detailed taxonomy of metadata types, based on this research, is provided. The taxonomy takes into account properties common to these types. Second, this work analyzes the state-of-the-art metadata collection techniques in literature and real-world educational content repositories including a showcase with the TUDelft library, in order to estimate the gap of metadata employment in the field of education. Following the results of this research and based on the observation that similar steps are often performed together, a set of easy-to-follow and generic enough design patters for generating metadata was identified. These design patterns aim at assisting content authors or data professionals with filling in metadata and thereafter, allowing for feature development or improvement in the respective platforms. The patterns for metadata extraction are based on the identified taxonomy of metadata. Finally, semantic metadata is extracted as proof of concept for two of the proposed patterns. A satisfactory to a high-quality result was achieved, showing that the patterns are intuitive and the data extracted with them, can be potentially used to describe the respective Educational Resource (ER) by adding the extracted information to its metadata.