Characterizing Students' Behavior Based on their Participation in Property Course in New Zealand

Abstract

Identifying students' learning behavior is very important in giving insights to the lecturer. Tracking data from 102 university students' in class and out of class have been investigated to find a different pattern in their learning process. Our aim was to group the students based on their activities in their class and their performance on the final exam. Data from students' answers to the regular quizzes were used at the end of online modules, internal test, and tournament questions. A clustering algorithm has been applied to the students' data to group them with similar performance and scores. 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.