  The prediction of student performance and the analysis of students' learning
behavior play an important role in enhancing online courses. By analysing a
massive amount of clickstream data that captures student behavior, educators
can gain valuable insights into the factors that influence academic outcomes
and identify areas of improvement in courses. In this study, we developed
ClickTree, a tree-based methodology, to predict student performance in
mathematical assignments based on students' clickstream data. We extracted a
set of features, including problem-level, assignment-level and student-level
features, from the extensive clickstream data and trained a CatBoost tree to
predict whether a student successfully answers a problem in an assignment. The
developed method achieved an AUC of 0.78844 in the Educational Data Mining Cup
2023 and ranked second in the competition. Furthermore, our results indicate
that students encounter more difficulties in the problem types that they must
select a subset of answers from a given set as well as problem subjects of
Algebra II. Additionally, students who performed well in answering end-unit
assignment problems engaged more with in-unit assignments and answered more
problems correctly, while those who struggled had higher tutoring request rate.
The proposed method can be utilized to improve students' learning experiences,
and the above insights can be integrated into mathematical courses to enhance
students' learning outcomes.
