Review on Application of Data Mining Educational Big Data
DOI:
https://doi.org/10.24113/ijosthe.v7i4.132Abstract
In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.
Metrics
References
Zhou Q, Mou C, Yang D. Research progress on educational data mining: A survey. Ruan Jian Xue Bao/Journal of Software, 2015, 26(11): 3026?3042 (in Chinese). http://www.jos.org.cn/1000-9825/ 4887.html.
Sun Xuejuan. Application of data mining technology in teaching information [J]. Information communication. 2019.277-278.
Amjed Abu Saa.Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques. Technology, Knowledge and Learning, 2019, Vol.24 (4), pp.567-598.
Tapani Toivonen. Augmented intelligence in educational data mining. Smart Learning Environments, 2019, Vol.6 (1), pp.1-25.
Shah J. Miah.Editorial note: Learning management systems and big data technologies for higher education. Education and Information Technologies: The Official Journal of the IFIP Technical Committee on Education, 2020, Vol.25 (1), pp.725-730.
L. Ji, X. Zhang and L. Zhang, "Research on the Algorithm of Education Data Mining Based on Big Data," 2020 IEEE 2nd International Conference on Computer Science and Educational Informatization (CSEI), Xinxiang, China, 2020, pp. 344-350.
L. Yu, X. Wu and Y. Yang, "An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm," in Chinese Journal of Electronics, Vol. 28, no. 1, pp. 21-28, 1 2019.
Z. Li, "New Employee Student Repast Big Data Analysis Research Application," 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos, 2020, pp. 583-586.
K. J. de O. Santos, A. G. Menezes, A. B. de Carvalho and C. A. E. Montesco, "Supervised Learning in the Context of Educational Data Mining to Avoid University Students Dropout," 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Macei??, Brazil, 2019, pp. 207-208.
S. S. Al-Nadabi and C. Jayakumari, "Predict the selection of mathematics subject for 11th grade students using Data Mining technique," 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), Muscat, Oman, 2019, pp. 1-4.
N. Ketui, W. Wisomka and K. Homjun, "Using Classification Data Mining Techniques for Students Performance Prediction," 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Nan, Thailand, 2019, pp. 359-363.
Z. Shao, H. Sun, X. Wang and Z. Sun, "An Optimized Mining Algorithm for Analyzing Students’ Learning Degree Based on Dynamic Data," in IEEE Access, Vol. 8, pp. 113543-113556, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Mr Rishiram, Sumit Sharma
This work is licensed under a Creative Commons Attribution 4.0 International License.
