COLOMBIAN CONFERENCE ON COMPUTING, 2018
Preparing high-quality learning material is a time-intensive, yet crucial task for teachers of all educational levels. In this paper, we present SoLeMiO, a tool to recommend and integrate learning material in popular authoring software. As teachers create their learning material, SoLeMiO identifies the concepts they want to address. In order to identify relevant concepts in a reliable, automatic and unambiguous way, we employ state of the art concept recognition and entity linking tools. From the recognized concepts, we build a semantic representation by exploiting additional information from Open Knowledge Graphs through expansion and filtering strategies. These concepts and the semantic representation of the learning material support the authoring process in two ways. First, teachers will be recommended related, heterogeneous resources from an open corpus, including digital libraries, domain-specific knowledge bases, and MOOC platforms. Second, concepts are proposed for semi-automatic tagging of the newly authored learning resource, fostering its reuse in different e-learning contexts. Our approach currently supports resources in English, French, and Spanish. An evaluation of concept identification in lecture video transcripts and a user study based on the quality of tag and resource recommendations yielded promising results concerning the feasibility of our technique.