An approach to metadata generation for learning objects

Abstract

Metadata describe instructional resources and define their nature and use. Metadata are required to guarantee reusability and interchange of instructional resources into e-Learning systems. However, fulfilment of large metadata attributes is a hard and complex task for almost all LO developers. As a consequence many mistakes are made. This can cause the impoverishment of data quality in indexing, searching and recovering process. We propose a methodology to build Learning Objects from digital resources. The first phase includes automatic preprocessing of resources using techniques from information retrieval. Initial metadata obtained in this first phase are then used to search similar LO to propose missed metadata. The second phase considers assisted activities that merge computer advice with human decisions. Suggestions are based on metadata of similar Learning Object using fuzzy logic theory. © 2010 Springer-Verlag.