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2017 | OriginalPaper | Chapter

Classification of Speaking Proficiency Level by Machine Learning and Feature Selection

Authors : Brendan Flanagan, Sachio Hirokawa, Emiko Kaneko, Emi Izumi

Published in: Emerging Technologies for Education

Publisher: Springer International Publishing

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Abstract

Analysis of publicly available language learning corpora can be useful for extracting characteristic features of learners from different proficiency levels. This can then be used to support language learning research and the creation of educational resources. In this paper, we classify the words and parts of speech of transcripts from different speaking proficiency levels found in the NICT-JLE corpus. The characteristic features of learners who have the equivalent spoken proficiency of CEFR levels A1 through to B2 were extracted by analyzing the data with the support vector machine method. In particular, we apply feature selection to find a set of characteristic features that achieve optimal classification performance, which can be used to predict spoken learner proficiency.

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previous chapter Developing a Speaking Practice Website by Using Automatic Speech Recognition Technology
next chapter Integrating Mind Tools and Peer Assessment for Assisting Students in Foreign Language Learning
Footnotes
Literature
1.
Izumi, E., Uchimoto, K., Isahara, H.: The NICT JLE Corpus. ACL Publishing (2004). (in Japanese)
2.
Izumi, E., Uchimoto, K., Isahara, H.: The NICT JLE corpus: exploiting the language learner’s speech database for research and education. Int. J. Comput. Internet Manag. 12(2), 119–125 (2004)
3.
Izumi, E., Uchimoto, K., Isahara, H.: The overview of the SST speech corpus of Japanese learner English and evaluation through the experiment on automatic detection of learners’ errors. In: 4th International Conference on Language Resources and Evaluation, pp. 1435–1438 (2004)
4.
Council of Europe: Common European Framework of Reference for Languages: Learning, Teaching, Assessment. Cambridge University Press, Cambridge (2001)
5.
Tono, Y. (ed.): The CEFR-J Handbook: A Resource Book for Using CAN-DO Descriptors for English Language Teaching. Taishukan Publishing (2013). (in Japanese)
6.
Page, E.B.: The use of the computer in analyzing student essays. Int. Rev. Educ. 14(2), 210–225 (1968)CrossRef
7.
Supnithi, T., Uchimoto, K., Saiga, T., Izumi, E., Virach, S., Isahara, H.: Automatic proficiency level checking based on SST corpus. In: Proceedings of the RANLP, pp. 29–33 (2003)
8.
Abe, M.: Frequency change patterns across proficiency levels in Japanese EFL learner speech. Apples: J. Appl. Lang. Stud. 8(3), 85–96 (2014)
9.
Flanagan, B., Hirokawa, S.: The relationship of English foreign language learner proficiency and an entropy based measure. IEE 1(3), 29–38 (2015)
10.
Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the ACM-KDD, pp. 217–226 (2006)
11.
Sakai, T., Hirokawa, S.: Feature words that classify problem sentence in scientific article. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, pp. 360–367 (2012)
Metadata
Title
Classification of Speaking Proficiency Level by Machine Learning and Feature Selection
Authors
Brendan Flanagan
Sachio Hirokawa
Emiko Kaneko
Emi Izumi
Copyright Year
2017
Book
Emerging Technologies for Education

Print ISBN: 978-3-319-52835-9

Electronic ISBN: 978-3-319-52836-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-52836-6_72

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