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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Naoual Chaouni Benabdellah
ENSIAS,Mohammed V University
Computer Science & IT Engineering

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Towards a semantic integration of data from learning platforms Khaoula Mrhar; Otmane Douimi; Mounia Abik; Naoual Chaouni Benabdellah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i3.pp535-544

Abstract

Nowadays, there is a huge production of Massive Open Online Courses MOOCs from universities around the world. The enrolled learners in MOOCs skyrocketed along with the number of the offered online courses. Of late, several universities scrambled to integrate MOOCs in their learning strategy. However, the majority of the universities are facing two major issues: firstly, because of the heterogeneity of the platforms used (e-learning and MOOC platforms), they are unable to establish a communication between the formal and non-formal system; secondly, they are incapable to exploit the feedbacks of the learners in a non-formal learning to personalize the learning according to the learner’s profile. Indeed, the educational platforms contain an extremely large number of data that are stored in different formats and in different places. In order to have an overview of all data related to their students from various educational heterogeneous platforms, the collection and integration of these heterogeneous data in a formal consolidated system is needed. The principal core of this system is the integration layer which is the purpose of this paper. In this paper, a semantic integration system is proposed. It allows us to extract, map and integrate data from heterogeneous learning platforms “MOOCs platforms, elearning platforms” by solving all semantic conflicts existing between these sources. Besides, we use different learning algorithms (Long short-term memory LSTM, Conditional Random Field CRF) to learn and recognize the mapping between data source and domain ontology.
Co-Authors Khaoula Mrhar Mounia Abik Otmane Douimi