No abstract

Personality and individual differences are effective parameters in human activities such as learning. Since the learning style of each learner is different, we must fit learning to the different needs of learners. In this paper, an innovative learning approach is proposed by considering the learner's preferences. Using the Myers-Briggs Type Indicator's (MBTI) tools, a framework for adaptive teaching strategies has been developed in e-learning context. Moreover, an experiment was conducted to evaluate the performance of our approach. The results of the system tested in real environments show that considering the learner's preferences increases learning quality and satisfies the learner.

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.

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 with each student. In fact from the knowledge his/her learning style; it is easier to recommend a learning scenario 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 system based on a psychological model of Felder and Solomon and the collaborative filtering techniques. Using the learner profile, the device proposes a personalize learning scenario by selecting the most appropriate learning objects. 