Title: Learning actor ontology for a personalised recommendation in massive open online courses

Authors: Sara Assami; Najima Daoudi; Rachida Ajhoun

Addresses: Department of Computer Science, Smart Systems Laboratory, Centre d'Etudes Doctorales en Sciences des Technologies de l'Information et de l'Ingénieur (ST2I), National School of Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Agdal, Rabat, Morocco ' Department of Data and Knowledge Engineering, SSL Laboratory, Information Sciences School (ESI), Rabat, Morocco ' Department of Computer Science, Smart Systems Laboratory, Centre d'Etudes Doctorales en Sciences des Technologies de l'Information et de l'Ingénieur (ST2I), National School of Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Agdal, Rabat, Morocco

Abstract: Massive Open Online Courses revolutionised online learning and instigated research on information and communication technologies for learning to enhance the learner experience and increase his engagement level. In earlier research, we identified the recommendation criteria that could be used to recommend suitable MOOCs for the learner's needs and motivations. Thus, criteria like the level of knowledge, competences and the pace of learning introduced by MOOCs and preferred by learners will be matched by a personalised recommender system. In this paper, we model the Learning Actor ontology to be used for this matching process. It is a domain ontology that was developed by following the phases of the on-to-knowledge methodology: feasibility study, kickoff phase, refinement phase, evaluation and validation phase and, finally, the maintenance phase. At last, we obtained the learning ontology that describes the "learning actor" major class by using a complex definition of characteristics and their relationship and range of values.

Keywords: MOOC; massive open online course; recommender system; personalised recommendation; learning actor ontology; on-to-knowledge methodology; QMOOCs; quality MOOCs; ontology engineering; domain ontology.

DOI: 10.1504/IJTEL.2020.110048

International Journal of Technology Enhanced Learning, 2020 Vol.12 No.4, pp.390 - 410

Received: 11 Feb 2019
Accepted: 20 Jun 2019
Published online: 02 Oct 2020 *

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