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A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Volume 14, Number 4, April 2018, pp. 639-646
DOI: 10.23940/ijpe.18.04.p6.639646

Chunqiao Mia,b, Qingyou Dengc, Jing Lina,b, and Xiaowu Denga,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
cHuman Resource Department, Huaihua University, Huaihua, 418000, China

(Submitted on February 1, 2018; Revised on February 26, 2018; Accepted on March 28, 2018)


As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies is a comprehensive result of various factors, which is characterized by uncertainty. To deal with this issue, fuzzy sets theory and fuzzy logic are advantageous compared with traditional methods. In this study, based on dynamic student study process data, a fuzzy synthetic evaluation method for dynamic early warning student study failure risk is provided. For each student, three specific early warning factors: 1) student course participation, 2) assignment earned points, and 3) student attendance record, are selected as risk indicators, and the overall risk level is determined by a fuzzy synthetic evaluation approach, which can dynamically give the situation of risk as the evaluation time point changes. Finally, our obtained results show that the employed method is good for identifying at-risk students and exploring the risk reasons by showing the degrees of each early warning factors to the overall risk level. It is of significance for educators to timely apply corresponding strategic pedagogical interventions to help at-risk students avoid academic failure.


References: 34

    1. H. Almarabeh, “Analysis of Students' Performance by Using Different Data Mining Classifiers,” International Journal of Modern Education & Computer Science, Vol. 9, No. 8, pp. 9-15, 2017.
    2. J. Bainbridge, J. Melitski, A. Zahradnik, E. Lauría, S. Jayaprakash, and J. Baron, “Using Learning Analytics to Predict At-Risk Students in Online Graduate Public Affairs and Administration Education,” Journal of Public Affairs Education, Vol. 21, No. 2, pp. 247-262, 2015.
    3. R. S. Baker, D. Lindrum, M. J. Lindrum, and D. Perkowski, “Analyzing Early At-Risk Factors in Higher Education E-Learning Courses,” in Proceedings of the 8th International Conference on Educational Data Mining, pp. 150-155, 2015.
    4. R. Barber, and M. Sharkey, “Course Correction: using Analytics to Predict Course Success,” in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada, New York, NY: Association of Computer Machinery, pp. 259-262, 2012.
    5. J. Bravo, S. Sosnovsky and A. Ortigosa, “Detecting Symptoms of Low Performance using Prediction Rules,” in Proceedings of the 2nd Educational Data Mining Conference, Universidad de Cordoba, Cordoba, Spain, pp. 31-40, 2009.
    6. J. P. Campbell, “Utilizing Student Data within the Course Management System to Determine Undergraduate Student Academic Success: an Exploratory Study,” Doctoral dissertation, Purdue University, pp. 31-61, 2007.
    7. K. A. Capao, A. D. Cantara, A. M. Ceniza, P. M. J. Eduardo, S. B. Polinar, and J. M. Tero, “Predicting Academic Performance with Intelligence, Study Habits and Motivation Factors using Naive Bayes Algorithm,” International Journal of Engineering Research & Technology, Vol. 5, No. 3, pp. 182-185, 2016.
    8. G. D. Chen, C. C. Liu, K. L. Ou, and B. J. Liu., “Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology,” Journal of Educational Computing Research, Vol. 23, No. 3, pp. 305-332, 2000.
    9. M. J. Chen, Q. Wang, Y.P. Zhang, and S. G. Duan, “An Exploration of Quality Evaluation System for University Classroom Teaching based on Fuzzy Mathematics,” Journal of the Hebei Academy of Sciences, No. 1, pp. 1-4, 2015.
    10. D. Detoni, C. Cechinel, R. A. Matsumura, and D. F. Brauner, “Learning to Identify At-Risk Students in Distance Education Using Interaction Counts,” Revista de Informática Teórica e Aplicada, Vol. 23, No. 2, pp. 124-140, 2016.
    11. A. Elbadrawy, A. Polyzou, Z. Ren, S. Mackenzie, K. George, and R. Huzefa, “Predicting Student Performance Using Personalized Analytics,” Computer, Vol. 49, No. 4, pp. 61-69, 2016.
    12. G. Geraldine, M. Colm, O. Philip, and H. Markus, “Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education,” Journal of Learning Analytics, Vol. 3, No. 2, pp. 330-372, 2016.
    13. A. K. Hamoud, A. M. Humadi, W. A. Awadh, and A. S. Hashim, “Students’ Success Prediction based on Bayes Algorithms,” International Journal of Computer Applications, Vol. 178, No. 7, pp. 6-12, 2017.
    14. C. Kevin, and A. David, “Utilizing Student Activity Patterns to Predict Performance,” International Journal of Educational Technology in Higher Education, Vol. 14, No. 1, pp. 1-15, 2017.
    15. H. Lin, “Based on Fuzzy Mathematics in College Teaching Quality Evaluation System,” Journal of Hunan Institute of Science & Technology, No. 4, pp. 25-27, 2012.
    16. Y. Ma, B. Liu, C. K. Wong, P. S. Yu, and S. M. Lee, “Targeting the Right Students using Data Mining,” in Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, USA, pp. 457-464, 2000.
    17. L. P. Macfadyen, and S. Dawson, “Mining LMS Data to Develop an Early Warning System for Educators: a Proof of Concept,” Computers & Education, Vol. 54, No. 2, pp. 588-599, 2010.
    18. H. Martin, Z. Zdenek and Z. Jaroslav, “Ouroboros: Early Identification of At-Risk Students without Models based on Legacy Data,” in Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, Canada, pp. 6-15, 2017.
    19. C. Mi, X. Peng, and Q. Deng, “An Artificial Neural Network Approach to Student Study Failure Risk Early Warning Prediction Based on TensorFlow,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 219. pp. 326-333, 2018.
    20. B. B. Minaei, and W. Punch, “Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System,” in Proceedings of Genetic and Evolutionary Computational Conference, Chicago, Illinois, USA, pp. 2252-2263, 2003.
    21. C. Mollica, and L. Petrella, “Bayesian Binary Quantile Regression for the Analysis of Bachelor-to-Master Transition,” Journal of Applied Statistics, Vol. 44, No. 15, pp. 2791-2812, 2017.
    22. L. V. Morris, S. Wu and C. Finnegan, “Predicting Retention in Online General Education Courses,” The American Journal of Distance Education, Vol. 19, No. 1, pp. 23-36, 2005.
    23. S. M. Muthukrishnan, M. K. Govindasamy, and M. N. Mustapha, “Systematic mapping review on student's performance analysis using big data predictive model,” Journal of Fundamental and Applied Sciences, Vol. 9, No.4S, pp. 730-758, 2017.
    24. F. Razaque, N. Soomro, S. A. Shaikh, S. Soomro, J. A. Samo, N. Kumar, and H. Dharejo, “Using Naive Bayes Algorithm to Students' bachelor Academic Performances Analysis,” in Proceedings of 4th IEEE International Conference on Engineering Technologies and Applied Sciences, Afyonkarahisar, Turkey, pp. 1-5, 2017.
    25. M. J. Sandeep, W. M. Erik, J. M. L. Eitel, R. R. James, and D. B. Joshua, “Early Alert of Academically At-Risk Students: an Open Source Analytics Initiative,” Journal of Learning Analytics, Vol. 1, No. 1, pp. 6-47, 2014.
    26. M. Tripathi, and A. K. Agarwal, “Probabilistic Determination of Student Performance using Naive Bayes Classification Algorithm,” International Journal of Engineering Science and Computing, Vol. 7, No. 8, pp. 14749-14752, 2017
    27. M. Xu, Y. Liang, and W. Wu, “Predicting Honors Student Performance Using RBFNN and PCA Method,” Lecture Notes in Computer Science, Vol. 10179, pp. 364-375, 2017.
    28. J. Xu, K. H. Moon, and M. V. D. Schaar, “A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs,” IEEE Journal of Selected Topics in Signal Processing, Vol. 11, No. 5, pp. 742-753, 2017.
    29. T. Y. Yang, C. G. Brinton, W. C. Joe, and M. Chiang, “Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks,” IEEE Journal of Selected Topics in Signal Processing, Vol. 11, No. 5, pp. 716-728, 2017.
    30. S. J. H. Yang, O. H. T. Lu, A. Y. Q. Huang, J. C..H. Huang, H. Ogata, and A. J.Q. Lin, “Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis,” Journal of Information Processing, Vol. 26, pp. 170-176, 2018.
    31. J. Yi, “Research and Application of Learning Status Evaluation based on Eye Movements,” Master dissertation, Shanghai Jiao Tong University, pp. 6-44.
    32. L. A. Zadeh, “Fuzzy Sets,” Information Control, No. 8, pp. 338-353, 1965.
    33. Q. Zhang, and L. Yang, “Learning Measurement Progress and Trends in e-Learning—based on Eye Movement Application Perspective,” Distance Education and Online Learning, Vol.11, pp. 68-73, 2016.
    34. Q. Zhang, F. Wu, and S. Lai, “Information Architecture of Learning Analytics Dashboards via Meta-analysis of the Eye Movement Data,” Open Education Research, Vol. 23, No. 6, pp. 94-103, 2017.


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