Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Abdelladim Hadioui, Yassine Benjelloun Touimi, Nour-eddine El Faddouli, Samir Bennani


Higher education is increasingly integrating free LMS (Learning Management Systems). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors (i.e., tutors, learners, designers, administrators, and so on), who use these technological systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: ontological mapping’, ontological learning, ontological enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches (i.e., word embedding based on word2vec, SVM classification and ontologies) throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.


artificial neurons networks; big data; E-learning; linked data; ontology engineering;

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ISSN 2088-8708, e-ISSN 2722-2578