Characterizing students based on their participation in the class

Abstract

With the increasing number of students in class, it is very important to give insights to the lecturer about how students are learning. In this study, clustering has been applied to the students’ class participation data to group them based on similar performance and scores. Participants were 102 second-year undergraduate students at a New Zealand university. The data include students’ responses to the regular quizzes and at the end of online modules questions, internal test, and tournament questions. Applying K-Means, four different groups of students have been identified. The results revealed that students who were more active and participated more in activities achieved better scores on their final exam.