Username   Password       Forgot your password?  Forgot your username? 


Data-Driven Student Learning Performance Prediction based on RBF Neural Network

Volume 15, Number 6, June 2019, pp. 1560-1569
DOI: 10.23940/ijpe.19.06.p7.15601569

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 March 20, 2019; Revised on April 5, 2019; Accepted on June 7, 2019)


Based on the reduction and redundancy methods, the reliability performance of the improved general series-parallel system is considered, assuming the connected components are identically independent and follow the general exponential lifetime model. To extend previous studies, the shape parameter is modified to obtain the reliability equivalence factors of the hot and cold duplications. A hybrid of the hot and cold duplication methods is also considered. Numerical results from a practical example are investigated to illustrate the derived theoretical results of the overall study.


References: 18

  1. C. Mi, Q. Deng, X. Peng, D. Yin, and Y. Liu, “The Business Process Optimization of Early Warning Education in Colleges and Universities —Taking H College for Example (in Chinese),” Modern Educational Technology, Vol. 28, No. 3, pp. 92-98, 2018
  2. S. Zheng, “An Investigation and Analysis of College English Students' Learning Situation in Our College (in Chinese),” Journal of Huizhou University, No. 3, pp. 86-92, 1988
  3. L. Razzaq, J. Patvarczki, S. F. Almeida, M. Vartak, M. Feng, N. T. Heffernan, et al., “The Assistment Builder: Supporting the Life Cycle of Tutoring System Content Creation,” IEEE Transactions on Learning Technologies, Vol. 2, No. 2, pp. 157-166, 2009
  4. S. K. Yadav, B. Bharadwaj, and S. Pal, “Data Mining Applications: A Comparative Study for Predicting Students' Performance,” International Journal of Innovative Technology & Creative Engineering, Vol. 1, No. 12, pp. 13-19, 2011
  5. T. M. Christian and M. Ayub, “Exploration of Classification using NB Tree for Predicting Students' Performance,” in Proceedings of the International Conference on Data and Software Engineering, pp. 1-6, Bandung, Indonesia, 2014
  6. C. Romero, M. I. López, J. M. Luna, and S. Ventura, “Predicting Students' Final Performance from Participation in On-Line Discussion Forums,” Computers & Education, Vol. 68, pp. 458-472, 2013
  7. S. T. Jishan, R. I. Rashu, N. Haque, and R. M. Rahman, “Improving Accuracy of Students' Final Grade Prediction Model using Optimal Equal Width Binning and Synthetic Minority over-Sampling Technique,” Decision Analytics, Vol. 2, No. 1, pp. 1-25, 2015
  8. U. B. Mat, N. Buniyamin, P. M. Arsad, and R. Kassim, “An Overview of using Academic Analytics to Predict and Improve Students' Achievement: A Proposed Proactive Intelligent Intervention,” in Proceedings of the IEEE 5th International Conference on Engineering Education, pp. 126-130, Selangor, Malaysia, 2013
  9. 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, pp. 2252-2263, Chicago, Illinois, USA, 2003
  10. J. P. Campbell, “Utilizing Student Data within the Course Management System to Determine Undergraduate Student Academic Success: An Exploratory Study,” Doctoral Dissertation, pp. 31-61, Purdue University, 2007
  11. 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, National University for Distance Education, Madrid, Spain, 2015
  12. 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
  13. J. Bravo, S. Sosnovsky, and A. Ortigosa, “Detecting Symptoms of Low Performance using Prediction Rules,” in Proceedings of the 2nd Educational Data Mining Conference, pp. 31-40, Universidad de Cordoba, Cordoba, Spain, 2009
  14. S. M. Jayaprakash, E. W. Moody, E. J. M. Lauría, J. R. Regan, and J. D. Baron, “Early Alert of Academically at-Risk Students: an Open Source Analytics Initiative,” Journal of Learning Analytics, Vol. 1, No. 1, pp. 6-47, 2014
  15. 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
  16. 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
  17. W. Xing, R. Guo, E. Petakovic, and S. Goggins, “Participation-based Student Final Performance Prediction Model Through Interpretable Genetic Programming: Integrating Learning Analytics, Educational Data Mining and Theory,” Computers in Human Behavior, Vol. 47, pp. 168-181, 2015
  18. C. Mi, Q. Deng, J. Lin, and X. Deng, “A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation,” International Journal of Performability Engineering, Vol. 14, No. 4, pp. 639-646, 2018


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

This site uses encryption for transmitting your passwords.