Weighting based approach for learning resources recommendations

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

A Big-Data Oriented Recommendation Method in E-learning Environment

E-learning is increasingly gaining popularity in organizational and institutional learning for its several benefits to learn anywhere, anytime, and anyplace. Therefore, explosive growth of E-learning has caused difficulty of locating appropriate learning activities for learner in this context, and it becomes relatively widespread learning method for learner. Several research in e-learning mainly focused on improving learner achievements based on recommendation technique. An ideal recommender system in e-learning environment should be built with both accurate and learning goals. To address this challenge, we propose a recommendation method based on machine learning technique. Based on this tool, a learning approach is designed to achieve personalized learning experiences by selecting the most appropriate learning activities. Moreover, some experiments were conducted to evaluate the performance of our approach. The results demonstrate that our method outperforms other state-of-the-art methods and reveals suitability of using recommender system in order to support online learning activities to enhance learning

E-LEARNING PERSONALIZATION BASED ON COLLABORATIVE FILTERING AND LEARNER’S PREFERENCE

Personalized e-learning based on recommender system is recognized as one of the most interesting research field in the education and teaching in this last decade, since, the learning style is specific for each student. In fact from the knowledge of his or her learning style; it is easier to recommend a teaching strategy builds around a collection of the most adequate learning objects to give a better return on the educational level. This work focuses on the design of a personalized e-learning environment based on collaborative filtering and learning styles. Using the learner profile, the device proposed a personalized teaching strategy by selecting and sequencing learning objects fitting with the learners’ learning styles. Moreover, an experiment was conducted to evaluate the performance of our approach. The result reveals the system effectiveness for which it appears that the proposed approach may be promising

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

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