Author(s)

Siqi Wang, Yurui Wang, Qi Zeng and Yixin Du

Corresponding Author

Siqi Wang

ABSTRACT

Most of primary and middle schools have established their own database, but not making the full use of it. Apart from student identity information and achievements, subject selection data and student behavior data deserve more research and analysis. First, there is a connection between the different types of education data, which enables educators to observe students learning status more objectively. Second, tapping students' preferences in subject selection under the new college entrance examination policy can assist educators in optimizing instructional design. To realize these, we present VisEdu, an interactive visual analytics system to help educators visualize student behavior data, learning effect data and subject selection data and provide students with the best guidance. In particular, this paper proposes a multi-factor fusion method of student personal learning assessment based on AHP model. It combines students’ performance fluctuations with their behavior, so as to present a most comprehensive feedback to educators. We demonstrate the usability of our framework with three case studies from real-world campus.

KEYWORDS

Primary and middle schools, Visual learning analytics, Student personal learning assessment