An Algorithm of the Rough-ISM Analysis Method

Tian Wei Sheu; Tzu Liang Chen; Ching Pin Tsai; Jian Wei Tzeng; Masatake Nagai

doi:10.4028/www.scientific.net/AMR.779-780.1693

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 779-780An Algorithm of the Rough-ISM Analysis Method

An Algorithm of the Rough-ISM Analysis Method

Abstract:

In this paper, an algorithm of the Rough-ISM analysis method is proposed. The Rough-ISM analysis method is the combination of the Rough set theory and Interpretive Structural Modeling (ISM). It is not only a simple research method, also a practical way to find the students misconceptions. In addition, the most important thing is that the analysis method can overcome fewer participates and problems through Rough set theory statistically. Through the analysis method, common misconceptions of whole class can be found and then and then supplying teaching path for teachers to conducting remedial teaching based on common misconceptions. Finally, a practical example is provided to make the calculate process more clearly.

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Periodical:

Advanced Materials Research (Volumes 779-780)

Pages:

1693-1696

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.779-780.1693

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Online since:

September 2013

Authors:

Tian Wei Sheu, Tzu Liang Chen, Ching Pin Tsai, Jian Wei Tzeng, Masatake Nagai

Keywords:

ISM, Misconceptions, Rough Set Theory (RST), Rough-ISM, Teaching Path

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References

[1] M.G. Hewson and P.W. Hewson, Effect of instruction using students' prior knowledge and conceptual change strategies on science learning, J. Res. Sci. Teach. 20 (1983) 731-743.

DOI: 10.1002/tea.3660200804

Google Scholar

[2] K.L. Wen, M. Nagai, T.C. Chang and H.C. Wang, An Introduction to Rough Set Theory and Application, Chuan Hwa, Taipei, (2008).

Google Scholar

[3] J.N. Warfield, Societal systems planning, policy and complexity, Wiley, New York, (1976).

Google Scholar

[4] J.N. Warfield, Interpretive structural modeling (ISM) Group Planning & Problem Solving Methods in Engineering, Wiley, New York, (1982).

Google Scholar

[5] K.L. Wen, M. Nagai, T.C. Chang and H.C. Wang, An Introduction to Rough Set Theory and Application, Chuan Hwa, Taipei, (2008).

Google Scholar

[6] Z. Pawlak, Rough sets, Int. J. Comput. Inf. Sci. 11 (1982) 341-356.

Google Scholar

[7] Z. Pawlak, Reasoning about data—A rough set perspective, Lect. Notes Comput. Sci. 1424 (1998) 25-34.

Google Scholar

[8] Z. Pawlak, Rough sets, decision algorithms and bayes' theorem, Eur. J. Oper. Res. 136 (2002) 181-189.

DOI: 10.1016/s0377-2217(01)00029-7

Google Scholar

[9] Z. Pawlak, Decision algorithms and ﬂow graphs; a rough set approach, J. Telecommun. Inf. Technol. 3 (2002) 98-101.

Google Scholar