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Student Performance Early Warning based on Data Mining

Volume 15, Number 3, March 2019, pp. 822-833
DOI: 10.23940/ijpe.19.03.p11.822833

Chunqiao Mia,b

aSchool of Computer Science and Engineering, Huaihua University, Huaihua, 418000, China
bKey Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua, 418000, China

(Submitted on October 23, 2018; Revised on November 25, 2018; Accepted on December 21, 2018)


Student performance in higher education is related to many complicated factors and always has uncertainty, so early warning of it is a very difficult issue. In this study, a systematic review was first carried out on student performance prediction and early warning using data mining techniques, including basic data sources, evaluating factors, predicting methods, application tools, and practices. Then, insufficiencies of the related studies were discussed, including incomprehensive source data, inadaptable and unspecialized calculation methods, and lack of integrated methodology systems in practice. Finally, a solution design was proposed, consisting of learning situation big data, a systematic early warning model, and an integrated information support system. Preliminary experiment results showed that it could identify at-risk students in a timely manner and improve the overall efficiency and effectiveness of early warning education management in practice, so it is of both academic and practical significance in promoting the deep integration of information technology and early warning education.


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