Analyzing Student Behavior in Online Programming Courses

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Rather than maintaining the classic teaching approach,a growing number of schools use the blended leing system in higher education.The traditional method of teaching focuses on the result of students progress.However,many student activities are recorded by an online programming leing platform at present.In this paper,we focus on student behavior when completing an online open-ended programming task.First,we conduct statistical analysis to examine student behavior on the basis of test times and completed time.By combining these two factors,we then classify student behavior into four types by using k-means algorithm.The results are useful for teachers to enhance their understanding of student leing and for students to know their leing style in depth.The findings are also valuable to re-design the leing platform.
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