Reference : Knowledge Graph-based Teacher Support for Learning Material Authoring

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/36645

Title :	Knowledge Graph-based Teacher Support for Learning Material Authoring
Language :	English
Author, co-author :	Grevisse, Christian [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Manrique, Rubén [Universidad de los Andes > Systems and Computing Engineering Department]
	Mariño, Olga [Universidad de los Andes > Systems and Computing Engineering Department]
	Rothkugel, Steffen [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	26-Sep-2018
Main document title :	Advances in Computing - CCC 2018
Publisher :	Springer
Collection and collection volume :	Communications in Computer and Information Science (Volume 885)
Peer reviewed :	Yes
Event name :	13th Colombian Conference on Computing (13CCC)
Event date :	from 26-09-2018 to 28-09-2018
Event organizer :	Colombian Computer Society (SCO2)
Event place (city) :	Cartagena
Event country :	Colombia
Keywords :	[en] Learning Material ; Authoring Support ; Knowledge Graph ; Concept Recognition
Abstract :	[en] 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.
Permalink :	http://hdl.handle.net/10993/36645
DOI :	10.1007/978-3-319-98998-3_14

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