On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018-Reference-Cited by-同舟云学术

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On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018

Published:2020-07-20 Issue:3 Volume:30 Page:371-430
ISSN:1560-4292
Container-title:International Journal of Artificial Intelligence in Education
language:en
Short-container-title:Int J Artif Intell Educ

Author:

Charitopoulos Angelos,Rangoussi Maria,Koulouriotis Dimitrios

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Education

Link

https://link.springer.com/content/pdf/10.1007/s40593-020-00200-8.pdf

Reference72 articles.

1. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.

2. Anderson, J. A. (1995). An introduction to neural networks. Boston, MA: MIT Press.

3. Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), 10.

4. Bajaj, V., Sharma, R. (2018). “Smart Education with artificial intelligence based determination of learning styles”. Procedia Computer Science 132, (pp. 834–842), International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Elsevier.

5. Baker, R. S. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed., pp. 112–118). Oxford, UK: Elsevier.

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