@article {Mrhar202072,
	title = {A dropout predictor system in moocs based on neural networks},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {14},
	number = {4},
	year = {2020},
	note = {cited By 2},
	pages = {72-80},
	abstract = {Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator{\textquoteright}s community as one of the most important problems. It{\textquoteright}s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90\% and the model with the feature of the learner{\textquoteright}s sentiments analysis attained average increase in AUC of 0.5\%. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.},
	doi = {10.14313/JAMRIS/4-2020/48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102757989\&doi=10.14313\%2fJAMRIS\%2f4-2020\%2f48\&partnerID=40\&md5=4cbaa0f7f458ce320541450af5ff8a46},
	author = {Mrhar, K. and Douimi, O. and Abik, M.}
}
@article {Mrhar2020535,
	title = {Towards a semantic integration of data from learning platforms},
	journal = {IAES International Journal of Artificial Intelligence},
	volume = {9},
	number = {3},
	year = {2020},
	note = {cited By 2},
	pages = {535-544},
	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{\textquoteright}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 {\textquotedblleft}MOOCs platforms, e-learning platforms{\textquotedblright} 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. {\textcopyright} 2020, Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijai.v9.i3.pp535-544},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090554399\&doi=10.11591\%2fijai.v9.i3.pp535-544\&partnerID=40\&md5=786ffdd85ffa5b0112c0728e4bb50266},
	author = {Mrhar, K. and Douimi, O. and Abik, M. and Benabdellah, N.C.}
}
@article {Mrhar20202934,
	title = {Towards optimize-ESA for text semantic similarity: A case study of biomedical text},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {10},
	number = {3},
	year = {2020},
	note = {cited By 1},
	pages = {2934-2943},
	abstract = {Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach. Copyright {\textcopyright} 2020 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijece.v10i3.pp2934-2943},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079383531\&doi=10.11591\%2fijece.v10i3.pp2934-2943\&partnerID=40\&md5=2965a06637b8b80abe9288b66e1082e7},
	author = {Mrhar, K. and Abik, M.}
}
@conference {Mrhar2017557,
	title = {Making MOOCs matter in formal education through a federating environment},
	booktitle = {Proceedings of the European Conference on e-Learning, ECEL},
	volume = {2010-October},
	year = {2017},
	note = {cited By 0},
	pages = {557-565},
	abstract = {With the emergence of open education and MOOCs, the opportunities and contribution of non-formal learning to the acquisition of knowledge and skills have increased. This type of learning, which has the advantage of being voluntary relying mainly on the learners{\textquoteright} motivation, remains technically invisible to formal learning environments. Finding suggest that the formal learning becomes increasingly less adapted to the learners needs because it does not take into consideration the real learner{\textquoteright}s profile (knowledge, skills, etc.) updated by non-formal learning. In order, to bridging formal and non-formal learning, we are aiming to personalize formal learning by recovering the learner{\textquoteright}s knowledge, abilities and skills which are acquired by non-formal learning (MOOCs). We propose in this paper, a federating environment for MOOCs hosted in different platforms such as (Coursera, open EdX..). The main objective of this environment is to provide to the formal learning environment a recommender system of MOOCs. The technical aspects of a federating environment of MOOCs (FEM) are presented. FEM is composed of an integration system and a recommender system of MOOCs. The integration system is responsible for integrating data emanating from different heterogeneous MOOCs platforms. The recommender system is based on the learners{\textquoteright} profiles and on the pedagogical objectives set by the concerned establishment that integrates the federating environment FEM. FEM also enables establishments to adapt the formal learning through the enriched learners{\textquoteright} profiles. {\textcopyright} The Authors, 2017.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037541912\&partnerID=40\&md5=345a1683d5b625a972e3de33841d44c8},
	author = {Mrhar, K. and Zary, N. and Abik, M.}
}
