Internet System for Mathematics Reading and Fuzzy Clustering Analysis for Students

Article Preview

Abstract:

The purpose of this study is to investigate the performance of mathematics reading based on fuzzy clustering. Mathematics reading proficiency is an importance issue which is related to the reading comprehension, mathematics achievement and mathematics literacy. Theoretical foundation of mathematics reading consists of three components, which are general reading comprehension, prior knowledge of mathematics and specific skills of mathematics. The subject is sixth graders. The researchers develop internet system of mathematics assessment and adopt fuzzy clustering to appropriately classify students. Results show that three clusters are the best and there exist characteristics and differences among clusters. Based on the findings, some recommendations and suggestions for future research are provided.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1719-1722

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] C. Fenwick: Humanistic Mathematics Network Journal Vol. 24 (2001) pp.52-58.

Google Scholar

[2] M. C. McKenna, and R. D. Robinson: Teaching through Text: Reading and Writing in the Content Areas (Boston, MA: Pearson Education 2009).

Google Scholar

[3] R. T. Vacca and J. A. I. Vacca: Content Area Reading: Literacy and Learning across the Curriculum (Boston, MA: Allyn and Bacon 2002).

Google Scholar

[4] K. D. Wood and D. B. Taylor: Literacy Strategies across the Subject Areas (Boston, MA: Pearson Education, Incorporated 2006).

Google Scholar

[5] M. L. Barton, C. Heidema and D. Jordan: Educational Leadership Vol. 60 (2002) pp.24-31.

Google Scholar

[6] C. C. Ragin: Fuzzy Set Social Science (Chicago, IL: University of Chicago Press 2000).

Google Scholar

[7] J. V. de Oliveira and W. Pedrycz: Advances in fuzzy clustering and its applications (New York: Wiley 2007).

Google Scholar

[8] J. O. Bullock: The American Mathematical Monthly Vol. 101 pp.735-743.

Google Scholar

[9] L. A. Zadeh: Information and Control Vol. 8 (1965) pp.338-353.

Google Scholar

[10] H. Zimmermann: Fuzzy Set Theory and Its Applications ( Boston: Kluwer Academic Publishers 2001).

Google Scholar

[11] C. J. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithm (NY: Plenum, 1981).

Google Scholar

[12] G. J. Klir, and B. Yuan: Fuzzy Sets and Fuzzy Logic: Theory and Applications (Upper Saddle River, NJ: Prentice Hall 1995).

DOI: 10.1021/ci950144a

Google Scholar

[13] Y. H. Lin, and J. M. Yih: Applied Mechanics and Materials, Vol. 55-57 (2011) pp.2197-2201.

Google Scholar