Analyzing and Predicting the TEM-4 Performance of English Majors in China

Yao Meng, Xiangdong Gu, Qing Zhou, Yu Zhong

Abstract

Test for English Majors-Band 4 (TEM-4) is a national Test for Chinese English majors in the end of their second year at university. This paper focuses on analysis and prediction of the TEM-4 performance of 77 English majors in a Chinese key university. A rich amount of data was collected including students’ demographics and family status, learning related achievement, motivation and learning journals they kept for a year with school’s permission and students’ willingness. The accuracy of three classification algorithms to predict students’ TEM-4 performance were compared and Naive Bayes Classifier is verified to gain the highest accuracy. On predicting whether the students’ TEM-4 scores might reach the excellent level, the accuracy of the model is above 90%. On predicting whether the students might pass the exam, the accuracy reaches 98%. One contributing finding of this study is that a richer set of data was collected, and we integrate the data. Another one is that students’ written learning journals have been verified in the improvement of the accuracy of the prediction model which hasn’t been explored in the previous researches about the test.

References

  1. Chuanyi Li. (2012). The criterion-related validation of TEM4 based on test-takers' self-assessment, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Pages: 856 - 858.
  2. Elliot, A. J., and McGregor, H. A. (2001). A 2× 2 achievement goal framework. Journal of personality and social psychology, 80(3), 501.
  3. Elliot, A. J., and Murayama, K. (2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100(3), 613.
  4. Hall, M. A. (1999). Correlation-based feature selection for machine learning. The University of Waikato.
  5. Li, L. (2013). Foreign Language Aptitude Components and Different Levels of Foreign Language Proficiency Among Chinese English Majors. In Pacific Rim Objective Measurement Symposium (PROMS) 2012 Conference Proceeding, pp. 179-196. Springer Berlin Heidelberg.
  6. Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol. 3, No. 22, pp. 41-46. IBM New York.
  7. Sun, J. Y., (2014). “Jieba Chinese text segmentation.” https://github.com/fxsjy/jieba, accessed July 21, 2014.
  8. Wen, Q. F., and Wang, L. (2009). Validation of TEM 4- Oral. Journal of PLA University of Foreign Languages, 5, 009.
  9. Xi, C. (2012). A Linear Regression Analysis on TEM4. Journal of Civil Aviation Flight University of China, 6, 020.
  10. Zhou Q, Mou C, Yang D. (2015) Research Progress on Educational Data Mining: A Survey. Ruan Jian Xue Bao/ Journal of Software, 26(11): 3026-3042(in Chinese).
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Paper Citation


in Harvard Style

Meng Y., Gu X., Zhou Q. and Zhong Y. (2017). Analyzing and Predicting the TEM-4 Performance of English Majors in China . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 256-261. DOI: 10.5220/0006263102560261


in Bibtex Style

@conference{csedu17,
author={Yao Meng and Xiangdong Gu and Qing Zhou and Yu Zhong},
title={Analyzing and Predicting the TEM-4 Performance of English Majors in China},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={256-261},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006263102560261},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Analyzing and Predicting the TEM-4 Performance of English Majors in China
SN - 978-989-758-239-4
AU - Meng Y.
AU - Gu X.
AU - Zhou Q.
AU - Zhong Y.
PY - 2017
SP - 256
EP - 261
DO - 10.5220/0006263102560261