Generating Personalized Navigation Learning Path Based on Knowledge Map

With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.