EDUCATIONAL FUZZY DATA-SETS AND DATA MINING IN A LINEAR FUZZY REAL ENVIRONMENT

Authors

  • Frank Rogers The University of West Alabama

DOI:

https://doi.org/10.30862/jhm.v2i2.81

Keywords:

Student Learning Environment, Educational Fuzzy Data-sets, Linear Fuzzy Real Numbers, Machine Learning

Abstract

Educational data mining is the process of converting raw data from educational systems to useful information that can be used by educational software developers, students, teachers, parents, and other educational researchers. Fuzzy educational datasets are datasets consisting of uncertain values. The purpose of this study is to develop and test a classification model under uncertainty unique to the modern student. This is done by developing a model of the uncertain data that come from an educational setting with Linear Fuzzy Real data. Machine learning was then used to understand students and their optimal learning environment. The ability to predict student performance is important in a web or online environment. This is true in the brick and mortar classroom as well and is especially important in rural areas where academic achievement is lower than ideal.

Author Biography

Frank Rogers, The University of West Alabama

Associate Professor in the Mathematics Department at The University of West Alabama

References

Ballestros, C. (2017). “Alabama has The Worst Poverty in the Developed World, U.N. Official Says,†Newsweek, accessed at https://www.newsweek.com/alabama-un-poverty-environmental-racism-743601.

Bellman, R.E., & Zadeh, L.A. (1970). Decision making in a fuzzy environment. Management Science, 17(4), B141–B164.

Cheng, J. (2017). Data-mining research in education. Report. Hongshan: International School of Software, Wuhan University.

Dubois, D., & Prade, H. (1982). System of linear fuzzy constraints. Fuzzy Sets and Systems, 13, 1–10.

Gareth, J., Witten, D., Hastie, T., & Tibshirani, R. (2015). An Introduction to Statistical Learning. New York: Springer.

Minikwa, N. (2017). Principle of Macroeconomics. Boston: Cengage Learning.

Monk, B. (2001). A proposed theory of fuzzy random variables. Dissertation. Tuscaloosa: University of Alabama.

Muller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. California: O’Reilly Media.

Neggers, J. & Kim, H. (2001). Fuzzy possets on sets. Fuzzy Sets and Systems, 117(3), 391-402.

Prevo, R. (2002). Entropies of families of fuzzy random variables: an introduction to an in-depth exploration of several classes of important examples. Dissertation. Tuscaloosa: University of Alabama.

Rogers, F. (2016). Linear fuzzy integers and Bezout’s identity. Asian Journal of Fuzzy and Applied Mathematics, 4(2), 5-10.

Rogers, F. (in press). Fuzzy gradient descent for the linear fuzzy real number system. AIMS Mathematics.

Stuckler, D., Basu, S., Suhrcke, M., Coutts, A., & McKee, M. (2009). The public health effect of economic crises and alternative policy responses in Europe: An empirical analysis. The Lancet, 374(9686), 315-323.

Well, D. N. (2007). Accounting for the effect of health on economic growth. The Quarterly Journal of Economics, 122(3), 1265-1306.

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Published

2019-08-08

How to Cite

Rogers, F. (2019). EDUCATIONAL FUZZY DATA-SETS AND DATA MINING IN A LINEAR FUZZY REAL ENVIRONMENT. Journal of Honai Math, 2(2), 77–84. https://doi.org/10.30862/jhm.v2i2.81

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