Abstract
In this paper, aiming at an objective evaluation of second language (L2) learners’ proficiencies, it was tried to predict the learners’ language proficiency using 94 statistics. The statistics were extracted automatically and manually from English conversation data collected from groups of Japanese English learners at educational institutions and were classified into 5 subcategories. To estimate the learners’ English proficiencies represented as Central European Framework of Reference (CEFR) Global Scale scores, canonical correlation analysis was performed on the statistics and the 5 subcategories, and their correlations to CEFR Global Scale scores were analyzed. As the result of the analysis, 24 statistics were selected for predicting the learners’ English proficiencies. The estimation experiment was carried out using a neural network trained by data set of 135 learners and the 24 statistics matrixes in cross-validation. An overall correlation of 0.894 was shown between the predicted proficiency scores and the L2 learners’ actual CEFR Global Scale scores. These results confirmed the usefulness of the 24 statistical measures out of the beginning set of 94 measures in the objective evaluation of L2 language proficiency.
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Hirabayashi, K., Nakagawa, S.: Automatic evaluation of English pronunciation by Japanese speakers using various acoustic features and pattern recognition techniques. In: Proceedings of INTERSPEECH, pp. 598–601 (2010)
Wang, H., Waple, C.J.: Kawahara, T.: Computer assisted language learning system based on dynamic question generation and error prediction for automatic speech recognition. J. Speech Commun. 51(10), 995–1005 (2009)
Nakamura, S., Matsuda, S., Kato, H., Tsuzaki, M., Sagisaka, Y.: Objective evaluation of English learners’ timing control based on a measure reflecting perceptual characteristics. In: Proceedings of IEEE ICASSP, pp. 4837–4840 (2009)
Nakamura, S., Kato, H., Sagisaka, Y.: Effects of Mora-timing in English rhythm control by Japanese learners. In: Proceedings of INTERSPEECH 2009, pp. 1539–1542 (2009)
Wang, H., Kawahara, T.: Effective prediction of errors by non-native speakers using decision tree for speech recognition-based CALL system. IEICE Trans. E92-D(12), 2462–2468 (2009)
Yasuda, K., Sumita, E., Yamamoto, S., Yanagida, M., Maekawa, K., Sugaya, F.: A proposal for automatically gauging of English language proficiency. IPSJ SIG Technical report, vol. 2003-NL-155, 65–70 (2003)
Kosuke, S., Masashi, S., Yuji, M.: Automatic estimation of English proficiency level using corpora. Information Processing Society of Japan SIG Technical report 2007-NL-181, 113–119 (2007)
Al-Barrak, M.A., Al-Razgan, M.: Predicting students final GPA using decision trees: a case study. Int. J. Inf. Educ. Technol. 6(7), 528–533 (2016)
Negishi, J.: Multi-faceted Rasch analysis for the assessment of group oral interaction using CEFR criteria. Ann. Rev. Engl. Lang. Educ. Jpn. 21, 111–120 (2010)
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Tsubaki, H. (2017). L2 Learners’ Proficiency Evaluation Using Statistics Based on Relationship Among CEFR Rating Scales. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_35
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DOI: https://doi.org/10.1007/978-3-319-61845-6_35
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