A AprioriAll Sequence Mining Algorithm Based on Learner Behavior

Abstract

Although massive learning resources in online education platform provide users with more learning opportunities, users are also faced with new challenges of information overload. At present, most of the personalized recommendation related research on educational resources is based on the campus application or the traditional online learning website design personalized recommendation algorithm for educational resources. It does not take into account the new characteristics of user behavior in online learning, and does not make full use of the collective wisdom embodied in the educational resources under the internet background.

In view of the shortcomings of personalized recommendation technology of educational resources, we put forward a learner model based on AprioriAll mining algorithm on the basis of analyzing the characteristics of user learning behavior in the Internet. It concretely attributes learners’ attributes and understands learners’ behaviors according to learner models. According to the established learner model, the learners’ behavior is tracked, and the potential relationship between courses is found through the use of sequence mining algorithm based on the behavior of the learners, and the courses that are more in line with the learners’ interest are recommended.