Mining Course Trajectories of Successful and Failure Students: A Case Study

被引：1
|
作者：
Yoo, Tin Soung [1 ]
Woo, Yei-Sol [1 ]
Park, Sang Jun [2 ]
机构：
[1] Indiana Univ Purdue Univ, Dept Comp Sci, Ft Wayne, IN 46805 USA
[2] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON BIG KNOWLEDGE (IEEE ICBK 2017) | 2017年
关键词：
Educational Data Mining (EDM); Sequential Pattern Mining; Course Enrollment Patterns; Dropout;
D O I：
10.1109/ICBK.2017.55
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Educational data mining (EDM) is an emerging interdisciplinary research area that uses computational approaches to answer education related questions. This study uses sequential pattern mining to solve two important problems in education: identifying course trajectories students take to earn an academic degree and discovering specific courses which may influence students' likelihood to divert from their original academic and career. For this study, we analyzed students in computer science and their course enrollment data, which was extracted from the Banner system at a public university. The preliminary results of the case study demonstrate the usefulness of sequential pattern mining in solving problems that are of interest to students, educators and administrators in post-secondary schools.
引用
收藏
页码：270 / 275
页数：6
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