Agent-based consultation support for learners in e-learning system

In the e-learning environment for self-study style, the means to solve learner's problems such as worry and uneasiness are very limited. In such environment, even if traditional consultation service is provided, the ability to response case by case is not enough in most cases. In real society, a learner trends to consult advisors such as teachers or familiar friends, when he has a problem which he cannot solve by himself. In this research, we proposed a new agent-based study support system, which chooses an appropriate advisor instead of the learner and adjust schedule plans between both of them. To achieve this system, advisor-choosing mechanism and schedule-adjusting mechanisms are implemented.