Volume 3 Number 5 (Oct. 2013)
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IJEEEE 2013 Vol.3(5): 397-401 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2013.V3.266

Using GRA and GSM Methods to Identify the Learning Strategies of Good Language Learners

B. T. Wang, H. T. Chen, and H. Y. Chang
Abstract— While learning a language, there are some learners who can learn it quickly, but come learners can’t. If we know more about the learning strategies of the successful language learners, we could use this information to enhance the poor language learner’s learning. The paper proposes to use the soft-computing methods: grey relational analysis (GRA) and grey structural modeling (GSM) to explore the learning strategy path of the good language learner. The purpose is to find the importance order of the learning strategies. Then both learners and teachers can adjust their learning and teaching styles in language acquisition. In order to quantify the data, 10 professional English teachers are interviewed, and their attitudes towards the learning strategies are calculated through the soft-computing methods. The results show that the planning strategy is the basic strategy of being a good language learner while communication strategy ranks the top. Overall, the results not only provide objective perceptions of good language learner’s learning strategy path, but the proposed soft-computing methods can be applied to the future decision-making fields.

Index Terms— GRA, GSM, good language learner, language acquisition.

Bor-Tyng Wang, Han-Tung Chen, and Hsueh-Yu Chang are with the Foreign Language Center, Feng-Chia University, Taiwan (e-mail: btwang.tw@gmail.com, vivianchensbs@yahoo.com, changhy@fcu.edu.tw).

Cite: B. T. Wang, H. T. Chen, and H. Y. Chang, " Using GRA and GSM Methods to Identify the Learning Strategies of Good Language Learners," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 3, no. 5, pp. 397-401, 2013.

General Information

ISSN: 2010-3654 (Online)
Frequency: Quarterly (Since 2015)
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, EI (INSPEC, IET)
E-mail: ijeeee@iap.org
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