Personalized Learning Path Recommendation Based On Memetic Algorithm

Due to the rapid development of internet technology, network has become one of the major ways for people to acquire knowledge. As a brand new learning pattern, E-learning via internet to teach and learn, fully utilizes a completely new communication system as well as a learning environment with abundant resources which provided by modern information technology. These bring flexibility and selectivity for learners either in time or space, providing a foundation for individualized learning, which makes more and more learners eager to gain knowledge individually and intelligently.However, most of the current network teaching systems are implemented by combining web-based learning material and some corresponding tools, which are simple application of internet technology, only providing certain static learning resources. All these not only lack of the function of teacher's on-time guidance, monitoring and regulation, etc; but also short of individual learning support which should adapt to the student's characteristics. Due to the huge amount and widely scattered network learning resources, there is a possibility that learners are easy to get lost and overloaded in the process of learning. And this brings out the subject that how should the learners to find out the best way to study according to their own knowledge structure and goal, with the limitations of time, cost, preference as well as enduring ability. As to these problems, this dissertation carries an in-depth research on individual learning model and learning path, put forward a recomm- endation system of individual learning path. In this way, it can satisfy the needs of various learners, recommending for them the most suitable way to learn as well as navigate intelligently in their learning path.At the very beginning, this dissertation goes deep to review the current development and future tendency of E-learning, penetrating into the theory and technology concerning about it. Such as: individualized service, learning path, ontology technology and knowledge map, etc. It provides a general framework of individualized learning model, has a research of the following areas as User Model, Individualized Recommendation Engine and Domain Database. Next this disserta- tion focuses on Memetic's overall structure, the solution representation and the design of genetic operators, etc. So to it, the author comes up with an improved Memetic algorithm. Thirdly, this thesis describes in detail the origin and shape of the learning path, putting forward a way of individualized evaluation, and the applic- ation of Memetic algorithm in the strategy of solution. In the end, based on the Memetic algorithm, it implements the systematic design and realization of personalized learning path, taking the computer network course as an instance, it gives out a simulating experiment, which bring about the analysis of the learner's self knowledge structure, goal and individual preference, all these finally lead to the realization of the goal of formulating individualized learning path for the learners.