Discovering student e-learning preferred navigation paths using selection page and time preference algorithm

Abstract

In this paper, the student navigation paths and student or visitor interested page is identified. Student navigation interest pattern mining contains both the frequently navigation path based on webpage memory size and session length .Relatively comparing access proportion of viewing time and selective page size, preference can be used for mining student learning pattern instead of interested subject. In order to identify Preferred Navigation Paths, an efficient algorithm for Visitor Access Matrix (VAM) by the page to page transition probabilities statistics of all visitor behaviors is introduced in this paper. Second, we propose an efficient algorithm for Selection and Time Preference (SATP) to identify the preference of web pages by viewing time. Third, the user interested page would calculate by both memory size and session. In this way we proposed the Preference of page content size and session identifier algorithm. The performance of the proposed algorithms is evaluated and the algorithms can determine preferred navigation path efficiently. The experimental results show the accuracy and scalability of the algorithms. This approach may be helpful in E-learmng, E-business, such as web personalization and website designer.