Characterizing Students’ Behavioral Patterns in an Online Reading Test

Abstract

Understanding students’ testing behaviors may help researchers design better computer-based assessment. For this reason, this study aims at characterizing students’ behavioral patterns in online reading test by k-means clustering. The clustering algorithm adopts eight indicators: reading time, answering time, the number of choosing articles, the number of choosing questions, the number of selecting options, the number of marking questions, the number of revisiting a test and the final testing scores. The result identifies five clusters of student testers: slow readers, fast readers, question markers, fast responders, and re-readers.