Groupized Learning Path Discovery Based on Member Profile

摘要

With the explosion of knowledge nowadays, it is urgent for people to learn new things quickly and effectively. To meet such a requirement, how we can find a suitable path for learning has become a crucial issue. Meanwhile, in our daily life, it is important and necessary for people from various backgrounds to achieve a certain task (eg. survey, report, business plan, etc.) collaboratively in the form of the group. For these group-based task, it often requires members to learn new knowledge by using e-learning system. In this paper, we focus on addressing the problem on discovering an appropriate study path to facilitate a group of people rather than a single person for effective learning under e-learning environment. Furthermore, we propose a group model to capture the expertise of each member. Based on this model, a groupized1 learning path discovering (GLPD) algorithm is proposed in order to help a group of learners to grasp new knowledge effectively and efficiently. Finally, we conduct a practical experiment whose result verifies the soundness of our approach.