Mining the Students´ Learning Interest in Browsing Web-Streaming Lectures

Web-streaming lectures overcome the space and time barriers between learning and teaching, but bring higher requirements on the learning feedback of students when they browse lectures. In this paper, we discover the students learning interest from their usage data in Web-based learning environment by using multi data mining methods. The learning interests are expressed in six questions, which were asked by the teachers. We use simple statistics, associate rules mining, multi linear regression and similarity comparing to answer different questions. The usage data of online learners are heterogeneous, including HTTP server logs and REAL Helix Universal logs, and these heterogeneous usage data are transformed into students browsing profiles. We implement our work on our Web-based learning environment: tele-TASK. The mined results help teachers to know their students clearly and adjust their teaching schedules efficiently