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All issues Volume 197 (2018) MATEC Web Conf., 197 (2018) 03006 References
Open Access
Issue
MATEC Web Conf.
Volume 197, 2018
The 3rd Annual Applied Science and Engineering Conference (AASEC 2018)
Article Number 03006
Number of page(s) 6
Section Computer Science
DOI https://doi.org/10.1051/matecconf/201819703006
Published online 12 September 2018
  1. L. C. Liñán, Á. Alejandro, and J. Pérez, “Educational Data Mining and Learning Analytics: differences, similarities, and time evolution Learning Analytics: Intelligent Decision Support Systems for Learning Environments,” RUSC. Univ. Knowl. Soc. J., vol. 12, no. 3, pp. 98-112, 2015. [CrossRef] [Google Scholar]
  2. Y. Yamasari, S. M. S. Nugroho, R. Harimurti, and M. H. Purnomo, “Improving the cluster validity on student’s psychomotor domain using feature selection,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, pp. 460-465. [CrossRef] [Google Scholar]
  3. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Pearson Addison Wesley, 2005. [Google Scholar]
  4. R. Asif, A. Merceron, and M. K. Pathan, “Predicting Student Academic Performance at Degree Level: A Case Study,” I.J. Intell. Syst. Appl. Intell. Syst. Appl., vol. 1, no. 1, pp. 49-61, 2015. [Google Scholar]
  5. N. A. Shukor, Z. Tasir, and H. Van der Meijden, “An Examination of Online Learning Effectiveness Using Data Mining,” in Procedia-Social and Behavioral Sciences, 2015, vol. 172, pp. 555-562. [CrossRef] [Google Scholar]
  6. B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting students performance in educational data mining,” in International Symposium on Educational Technology, ISET 2015, 2016. [Google Scholar]
  7. A. Pradeep, S. Das, and J. J. Kizhekkethottam, “Students dropout factor prediction using EDM techniques,” in Proceedings of the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015, 2015. [Google Scholar]
  8. A. A. Yahya, “Swarm intelligence-based approach for educational data classification,” J. King Saud Univ.-Comput. Inf. Sci., 2017. [Google Scholar]
  9. L. Ge, H. Tang, Q. Zhou, Y. Tang, and J. Lang, “Classification Algorithms to Predict Students’ Extraversion-Introversion Traits,” in 2016 International Conference on Cyberworlds (CW), 2016, pp. 135-138. [CrossRef] [Google Scholar]
  10. K. Kongsakun, “An improved recommendation model using linear regression and clustering for a private university in Thailand,” in 2013 International Conference on Machine Learning and Cybernetics, 2013, pp. 1625-1630. [CrossRef] [Google Scholar]
  11. F. Widyahastuti and V. U. Tjhin, “Predicting students performance in final examination using linear regression and multilayer perceptron,” in 2017 10th International Conference on Human System Interactions (HSI), 2017, pp. 188-192. [CrossRef] [Google Scholar]
  12. K. Casey and D. Azcona, “Utilizing student activity patterns to predict performance,” Int. J. Educ. Technol. High. Educ., vol. 14, no. 1, p. 4, Dec. 2017. [CrossRef] [Google Scholar]
  13. F. Grivokostopoulou, I. Perikos, and I. Hatzilygeroudis, “An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance,” Int. J. Artif. Intell. Educ., vol. 27, no. 1, pp. 207-240, Mar. 2017. [CrossRef] [Google Scholar]
  14. R. Harimurti, Y. Yamasari, Ekohariadi, Munoto, and B. I. G. P. Asto, “Predicting student’s psychomotor domain on the vocational senior high school using linear regression,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, pp. 448-453. [CrossRef] [Google Scholar]
  15. Y. Yamasari, S. M. S. Nugroho, I. N. Sukajaya, and M. H. Purnomo, “Features extraction to improve performance of clustering process on student achievement,” in 2016 International Computer Science and Engineering Conference (ICSEC), 2016, pp. 1-5. [Google Scholar]
  16. D. Freedman, Statistical models: theory and practice. Cambridge University Press, 2009. [CrossRef] [Google Scholar]
  17. R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58. WileyRoyal Statistical Society, pp. 267-288, 1996. [Google Scholar]
  18. A. E. Hoerl, R. W. Kennard, and R. W. Hoerl, “Practical Use of Ridge Regression: A Challenge Met,” Appl. Stat., vol. 34, no. 2, p. 114, 1985. [CrossRef] [Google Scholar]
  19. N. R. Draper and R. C. van Nostrand, “Ridge Regression and James-Stein Estimation: Review and Comments,” Technometrics, vol. 21, no. 4, p. 451, Nov. 1979. [CrossRef] [Google Scholar]

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