@article {Benabbes202261,
	title = {New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS},
	journal = {International Journal of Interactive Mobile Technologies},
	volume = {16},
	number = {19},
	year = {2022},
	note = {cited By 0},
	pages = {61-80},
	abstract = {Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identifies learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As the lack of good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style {\textcopyright} 2022, International Journal of Interactive Mobile Technologies.All Rights Reserved.},
	doi = {10.3991/ijim.v16i19.33733},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140748669\&doi=10.3991\%2fijim.v16i19.33733\&partnerID=40\&md5=4c17d51043684f46667ce010388153ce},
	author = {Benabbes, K. and Hmedna, B. and Housni, K. and Zellou, A. and El Mezouary, A.}
}
@article {Benabbes20221017,
	title = {Recommendation System Issues, Approaches and Challenges Based on User Reviews},
	journal = {Journal of Web Engineering},
	volume = {21},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {1017-1054},
	abstract = {With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user{\textquoteright}s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users{\textquoteright} perceptions. Recommender systems (RSs) apply various approaches to predict users{\textquoteright} interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users{\textquoteright} trust. {\textcopyright} 2022 River Publishers. All rights reserved.},
	doi = {10.13052/jwe1540-9589.2143},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131073458\&doi=10.13052\%2fjwe1540-9589.2143\&partnerID=40\&md5=56b7da2c9aa6d4c44328fa667db5ef0a},
	author = {Benabbes, K. and Housni, K. and El Mezouary, A. and Zellou, A.}
}
