Mining Educational Data to Predict Academic Dropouts: a Case Study in Blended Learning Course

被引：0

作者：

Sukhbaatar, Otgontsetseg ^{[1
]}

Ogata, Kohichi ^{[1
]}

Usagawa, Tsuyoshi ^{[1
]}

机构：

[1] Kumamoto Univ, Grad Sch Sci & Technol, Kumamoto, Japan

来源：

PROCEEDINGS OF TENCON 2018 - 2018 IEEE REGION 10 CONFERENCE | 2018年

关键词：

dropout prediction; educational data mining; e-learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Learning management systems generate a large amount of data, where knowledge discovery is possible using data mining techniques. We proposed simple prediction scheme using decision tree analysis for purpose of classification to identify dropout prone students in the middle of the semester based on previous year's course characteristics for that course. The data included 717 students' online activities in compulsory, sophomore level course with blended learning styles, 79% of the actual dropout students were predicted correctly.

引用

页码：2205 / 2208

页数：4

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