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Outmane Bourkoukou
Cadi Ayyad University, Marrakesh, Morocco
Computer Science & IT

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Weighting based approach for learning resources recommendations Outmane Bourkoukou; Omar Achbarou
JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.964 KB) | DOI: 10.30630/joiv.2.3.124

Abstract

Personalized e-learning systems based on recommender systems refines enormous amount of data and provides suggestions on learning resources which is appealing to the learner. Although, the recommender systems depends on content based approach or collaborative filtering technique to make recommendations, these methods suffers from cold start and data sparsity problems. To overcome the limitations of the aforementioned problems, a weight based approach is proposed for better performance. The main criterion for building a personalized recommender system is to exploit useful content and provide better recommendations with minimal processing time. The proposed system is a web based client side application which uses user profiles to form neighborhoods and calculates predictions using weights. For newcomers a profile is constructed based on learning styles. The resources which might be of interest to the user are predicted from calculated predictions.
Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques Outmane Bourkoukou; Essaid El Bachari
JOIV : International Journal on Informatics Visualization Vol 2, No 4 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1444.661 KB) | DOI: 10.30630/joiv.2.4.158

Abstract

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.
Co-Authors Essaid El Bachari Omar Achbarou