CES  >> Vol. 5 No. 2 (May 2017)

    Predicting At-Risk Students Based on the Campus Card and Students’ Basic Information

  • 全文下载: PDF(924KB) HTML   XML   PP.143-152   DOI: 10.12677/CES.2017.52024  
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王 勇,许 蕊:中国海洋大学,山东 青岛

校园一卡通学生基本信息机器学习Campus Card Student’s Basic Information Machine Learning



Accurate prediction of at-risk students in freshmen is extremely important to improve the graduation rate of a university. This paper explores the relationships between the campus card records and the performance of students. In addition, combining with the basic information of students and their previous exam scores, the paper proposes a method of predicting at-risk students based on machine learning and statistics. The experiment conducts on a dataset with 4.194 million items of 3680 freshmen of grade 2013 from the Ocean University of China, and the result shows that the recall rate is 52% and the precision is 77%, with good practical performance.

王勇, 许蕊. 基于校园一卡通与学生基本信息预测落后生[J]. 创新教育研究, 2017, 5(2): 143-152. https://doi.org/10.12677/CES.2017.52024


[1] 袁安府, 张娜, 沈海霞. 大学生学业预警评价指标体系的构建与应用研究[J]. 黑龙江高教研究, 2014(3): 79-83.
[2] Wu, H.F., Cheng, Y.S. and Hu, X.G. (2012) Model Design of Achievement Pre-Warning in High Education Based on Data Mining. Springer, Berlin Heidelberg, 501-506.
[3] Ramaswami, M. and Bhaskaran, R. (2010) A CHAID Based Performance Prediction Model in Educational Data Mining. International Journal of Computer Science Issues, 7, 10-18.
[4] Romero, C., Pez, M.I., Luna, J.M., et al. (2013) Predicting Students’ Final Performance from Participation in On-Line Discussion Forums. Computers & Education, 68, 458-472.
[5] Jovanovic, M., Vukicevic, M., Milovanovic, M., et al. (2012) Using Data Mining on Student Behavior and Cognitive Style Data for Improving e-Learning Systems: A Case Study. International Journal of Computational Intelligence Systems, 5, 597-610.
[6] Şen, B., Uçar, E. and Delen, D. (2012) Predicting and Analyzing Secondary Education Placement-Test Scores: A Data Mining Approach. Expert Systems with Applications, 39, 9468-9476.
[7] Minaeibidgoli, B., Kashy, D.A., Kortemeyer, G., et al. (2003) Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-Based System. Frontiers in Education, Westminster, 5-8 November 2003, T2A-13.
[8] Rovai, A.P. (2003) In Search of Higher Persistence Rates in Distance Education Online Programs. Internet & Higher Education, 6, 1-16.
[9] Marbouti, F., Diefes-Dux, H.A. and Madhavan, K. (2016) Models for Early Prediction of At-Risk Students in a Course Using Standards-Based Grading. Computers & Education, 103, 1-15.
[10] Pang, N., Michaelsteinbach, V. 数据挖掘导论: 完整版[M]. 北京: 人民邮电出版社, 2011: 89-451.
[11] Kubat, M. (2015) An Introduction to Machine Learning. Springer International Publishing, Berlin, 19-274.
[12] Edition, S. (2016) Applied Logistic Regression Analysis. Technometrics, 38, 184-186.
[13] Trappey, C.V. and Wu, H.Y. (2008) An Evaluation of the Time-Varying Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Product Lifecycles. Advanced Engineering Informatics, 22, 421-430.