Data-driven Analysis of Children’s Digital Reading Behavior: System Design and Application

Tracking, analyzing and guiding users' reading behavior based on big data indirectly is one of the key issues that need to be solved when digital reading moving towards intelligent reading. Currently, the digital reading platforms widely used for children have problems in monitoring the reading process and providing targeted feedback. Based on analysis of relevant research on digital reading behavior and children’s reading characteristics, this paper built a data model of digital reading behavior to collect and describe the data from multi-source and heterogeneous reading platforms. Then, the paper proposed an analytical framework for data of digital reading behavior, and visualized the analysis results from different dimensions and granularities. Finally, the paper developed the data analysis prototype system for digital reading behavior and took the data of more than 1,600 students, recorded in the practical application, as an example to carry out empirical research. This study can provide technical tools and methods for optimizing children’s digital reading platform.