E-Learning personalization based on hybrid recommendation strategy and learning style identification (original) (raw)
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In recent years, the internet has witnessed an aggressive growth in the amount of learning resources. This explosion of learning resources on the internet results in expanded interest for online learning resources by learners in e-learning environment. With this expansion of online learning resources, learners are experiencing challenges in deciding learning resources that are valuable and significant to their learning needs. Recommender systems can overcome this issue by filtering out inappropriate learning resources and automatically recommending suitable resources to the learners according to their interests. In this paper we are focusing on a course recommender system in an e-learning platform which tries to intelligently recommend courses to the learners based on their interest. This recommendation approach is used to provide learners some suggestions when they have trouble in choosing correct courses. It also allows us to study the behavior of learner regarding their course selection and suggests the best combination of courses in which the learners are interested.
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Personalized e-learning implementation is recognized one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different we must to fit elearning to the different needs of learners. This paper discusses teaching strategies matching with learner's personality using the Myers-Briggs Type Indicator (MBTI) tools. Based on an innovative approach, a framework for building an adaptive learning management system by considering learner's preference has been developed. The learner's profile is initialized according to the results obtained by the student in the index of learning styles questionnaire and then fine-tuned during the course of the interaction using the Bayesian model. 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.
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Nowadays, new technologies and the fast increase of the Internet have made access to information easier for all kinds of people, building new challenges for education when utilizing the Internet as a tool. E-Learning provide students with choices and initiative, however, results in much challenge in matching the needs of students with different backgrounds and learning preferences due to information overload. Facing diverse learning resources, students have difficulties in making appropriate choices to meet their learning objectives. One of the best examples is how to personalize an E-Learning system according to the learner's requirements and knowledge level in a learning process. This system should adapt the learning experience, according to the goals of the individual learner. In this paper, we present a recommender E-Learning approach which utilizes recommendation techniques for educational data mining specifically for identifying E-Learners' learning preferences. E-Learning recommendation system helps learners to make choices without sufficient personal experience of the alternatives, and it is considerably requisite in this information explosion age. In our study, the user-based collaborative filtering method is chosen as the primary recommendation algorithm, combined with online education. We analyze the requirement of a web based E-Learning recommendation system. The proposed system is based on four modules, namely Web search Module, student Profiling Module, Behavioral Activity analyzer module, recommendation module. The web search Module is a way of searching anything that user or student wants from Google search engine. A student Profiling Module takes Students all Personal and Academic Information, Behavioral Activity analyzer module is used to identify learners learning preferences and all activities which are done at the time of web surfing by students and a recommendation module which pre-processes data to create a suitable recommendation list and predicting the student interest domain. After recommendation process we calculate the Knowledge Point (KP) of particular student based on KP value it categories the student into three levels 1. Beginner 2. Intermediate 3. Master. Several techniques such as classification, clustering and association rules are used to improve personalization with filtering techniques to provide a recommendation and assist learners to improve their performance.