A Recommender Model in E-learning Environment

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

“…In our experimentation, the final exam for each course is considered to be acquired by the learner, if he obtains a score of control higher than 10/20. The learners' scores can be interpreted according to the percentage of correct answers, as follows: (excellent) (18)(19)(20), (very good), (16-18) (good), (14-16), (average) (10)(11)(12)(13)(14) and (marginal) (<10). Several studies suggest that students' satisfaction is an important factor in order to measure the effectiveness success of the E-learning process.…”

“…In last decade, a number of Learning Recommender Systems (LRSs) based on collaborative filtering have been introduced in order to support learners to achieve specific learning needs. Nevertheless, considering the various existing digital learning objects, such frameworks, could potentially play an important educational role [10,14,15].…”

“…Bourkoukou et al [10], propose a recommender model for e-learning environment to achieve personalized learning experiences by selecting and sequencing the most appropriate learning objects. By using a hybrid recommender system based on collaborative filtering technique and association rule mining algorithm.…”

“…This step is carried out by adapting the most known classifier algorithm K-NN on E-learning scenario [7,10]. This technique allows finding predictions by using the following steps:…”

“…On the other hand, the enormous increase of Learning Objects (LOs) and Learning Objects Repositories (LORs) on Internet in this last decade, are inspiring some researchers to design and to develop the specific recommender systems in distance learning [2,3,8,9,10].…”