Integrating Clustering and Sequential Analysis to Explore Students' Behaviors in an Online Chinese Reading Assessment System

It is helpful for students and teachers to identify students reading abilities and testing strategies based on their behavioral records of reading tests in an online Chinese reading assessment system. In this study, a K-means clustering algorithm is used to divide students into three potential clusters, and the behavioral sequence diagram of each cluster is drawn by means of the lag sequential analysis. By comparing the characteristics and differences of clusters, this paper draws the following main conclusions: (1) For better reading performance, increasing the time of reading articles is more beneficial than directly searching for the answers in the articles according to questions and options; (2) Students with high reading abilities spend longer time on reading articles and inspecting items, but rarely alter options; (3) Students with low reading abilities, who spend longer testing time and have more behaviors of clicking on articles and items, are not focused enough on current questions; (4) Those students with low reading abilities, who spend shorter testing time, rarely have inspection behaviors. Finally, this paper puts forward some suggestions based on the reading ability and testing strategy of each cluster to improve students reading literacy and instruct teachers reading teaching activities.