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Measuring Similarity to Observe Learners’ Syntactic Awareness in Web-Based Writing Environments

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11841))

Abstract

Writing in a foreign language is a struggle for learners and revising their writings is time consuming for teachers as well. For this reason, writing support systems have been widely proposed and one of its main functions is to automatically detect and revise errors in learners’ writings. However, the detection technologies are a work in progress and the effectiveness of error revision feedback is arguable. Meanwhile, numerous efforts have been made to enhance learners’ writing proficiency and reduce errors. Reading is considered as one of the important strategies. However, few studies have reported the linguistic knowledge that learners pay attention to and how they use the knowledge of web-based learning in their writings. In this paper, we performed a reading-to-write experiment in a web-based writing environment and analyzed reading materials and learners’ writings to explore how to observe learners’ awareness of syntactic structures in materials. Sentence patterns, proposed in our previous studies, have been introduced to categorize sentences, and the syntactic similarities between reading materials and learners’ writings have been calculated. The experimental results revealed that students showed higher comprehension of content but displayed poor attention towards syntactic structures in reading activities, if the structures were not significantly salient. It is assumed that the similarity measure is effective in observing students’ awareness of syntactic structures in materials, and further studies are needed to automatically observe the awareness.

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References

  1. Kimura, H., Kimura, T., Shiki, O.: Theory and practice in reading and writing: nurturing independent learning. Taishukan Publishing Co., Ltd., Tokyo (2010). (in Japanese)

    Google Scholar 

  2. Leacock, C., Chodorow, M., Gamon, M., Tetreault, J.: Automated Grammatical Error Detection for Language Learners, 2nd edn. Morgan & Claypool Publisher, San Rafael (2014)

    Google Scholar 

  3. Bowerman, C.: Writing and the computer: an intelligent tutoring systems solution. Comput. Educ. 18(1–3), 77–83 (1992)

    Article  Google Scholar 

  4. Yeh, S., Lo, J.: Using online annotations to support error correction and corrective feedback. Comput. Educ. 52(4), 882–892 (2009)

    Article  Google Scholar 

  5. Kunichika, H., Koga, T., Deyama, T., Murakami, T., Hirashima, T., Takeuchi, A.: Learning support for English composition with error visualization. Trans. Inf. Syst. (Jpn. Ed.) 91(2), 210–219 (2008)

    Google Scholar 

  6. Wilson, J., Czik, A.: Automated essay evaluation software in English Language Arts classrooms: effects on teacher feedback, student motivation, and writing quality. Comput. Educ. 100, 94–109 (2016)

    Article  Google Scholar 

  7. Truscott, J.: The case against grammar correction in L2 writing classes. Lang. Learn. 46(2), 327–369 (1996)

    Article  Google Scholar 

  8. Chandler, J.: The efficacy of various kinds of error feedback for improvement in the accuracy and fluency of L2 student writing. J. Second Lang. Writ. 12(3), 267–296 (2003)

    Article  Google Scholar 

  9. Van der Kleij, F.M., Feskens, R.C.W., Eggen, T.J.H.M.: Effects of feedback in a computer-based learning environment on students’ learning outcomes: a meta-analysis. Rev. Educ. Res. 85(4), 475–511 (2015)

    Article  Google Scholar 

  10. Ackerman, J.M.: Reading, writing, and knowing: the role of disciplinary knowledge in comprehension and composing. Res. Teach. Engl. 25(2), 133–178 (1991)

    MathSciNet  Google Scholar 

  11. Ito, F.: L2 reading–writing correlation in Japanese EFL high school students. Lang. Teach. 35(5), 23–29 (2011)

    Article  Google Scholar 

  12. Park, Y., Warschauer, M.: Syntactic enhancement and second language literacy: an experimental study. Lang. Learn. Technol. 20(3), 180–199 (2016)

    Google Scholar 

  13. Kawamura, K., Kashiwagi, H., Kang, M.: An approach toward automatic error detection in learners’ English writing based on the source language. In: 10th International Proceedings on Mobile, Hybrid, and On-Line Learning, pp. 62–65. IARIA, Roma (2018)

    Google Scholar 

  14. Delaney, Y.A.: Investigating the reading-to-write construct. J. Engl. Acad. Purp. 7, 140–150 (2008)

    Article  Google Scholar 

  15. Kuhbandner, C., Pekrun, R.: Joint effects of emotion and color on memory. Emotion 13(2), 375–379 (2013)

    Article  Google Scholar 

  16. Gali, N., Mariescu-Istodor, R., Hostettler, D., Fränti, P.: Framework for syntactic string similarity measures. Expert Syst. Appl. 129, 169–185 (2019)

    Article  Google Scholar 

  17. Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Meeting of the Association for Computational Linguistics, pp. 423–430 (2003)

    Google Scholar 

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Acknowledgement

This work is supported by JSPS KAKENHI Grant Number JP17K01081.

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Correspondence to Min Kang .

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Kang, M., Kawamura, K., Shao, S., Kashiwagi, H., Ohtsuki, K. (2019). Measuring Similarity to Observe Learners’ Syntactic Awareness in Web-Based Writing Environments. In: Herzog, M., Kubincová, Z., Han, P., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2019. ICWL 2019. Lecture Notes in Computer Science(), vol 11841. Springer, Cham. https://doi.org/10.1007/978-3-030-35758-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-35758-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35757-3

  • Online ISBN: 978-3-030-35758-0

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