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Accurate language achievement prediction method based on multi-model ensemble using personality factors

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Abstract

To overcome the noise in personality factors and precisely predict language achievement, we propose a robust regression algorithm based on the maximum correntropy criterion (MCC) and the coarse-to-fine method. Firstly, as there are many samples while few personality factors correlate to the language achievement in the data set, we propose a regression method based on Pearson feature selection to eliminate the noise and redundant features for solving the overfitting problem. Secondly, as the learning ability of each traditional regression model is different and limited, we introduce the model ensemble method based on MCC to predict language achievement via personality factors. Thirdly, owing to the fact that the language achievement data is unevenly distributed and the same model parameter cannot fit all the data effectively, we propose a coarse-to-fine prediction method to reduce prediction errors, which divides the range of the language achievement into multiple intervals and then establishes different regression models at each interval to obtain more accurate results. The experimental results on the data set of the personality factors and English achievement demonstrate the high precision and robustness of the proposed algorithm compared with the traditional single regression models.

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References

  1. Berkey CS, Hoaglin DC, Mosteller F (1995) A random-effects regression model for meta-analysis. Stat Med 14(4):395–411

    Article  Google Scholar 

  2. Blick G (1996) Personality traits. Learning strategies and performance1. Eur J Personal 10(4):1231

    Google Scholar 

  3. Butcher HJ, Ainsworth M, Nesbitt JE (1963) Personality factors and school achievement: a comparison of British and American children. Br J Educ Psychol 33(3):276–285

    Article  Google Scholar 

  4. Cattell HE (2001) The sixteen-personality factor (16PF) questionnaire. In: Understanding psychological assessment, pp 187–215

  5. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126

    Article  Google Scholar 

  6. Da H (2007) On the structural model of factors affecting CET4 scores of non-english majors. Psychol Sci 30:676–679

    Google Scholar 

  7. Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems, pp 1–15

  8. Ding D, Ji Z, Wang Y (2018) Research on surface roughness prediction method based on composite penalty regression model. Modern Physics Letters B 32

  9. Ephraim Y, Malah D (1984) Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans Acoust Speech Signal Process 32(6):1109–1121

    Article  Google Scholar 

  10. Eysenck HJ (1954) The structure of human personality. J Consult Psychol 18(1):75

    Google Scholar 

  11. Eysenck HJ (1972) Primaries or second-order factors: a critical consideration of Cattell’s 16 pf battery. British Journal of Social and Clinical Psychology 11(3):265–269

    Article  Google Scholar 

  12. Fatahi S, Moradi H, Kashani-Vahid L (2016) A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments. Artif Intell Rev 46(3):413–429

    Article  Google Scholar 

  13. Friedman AF, Sasek J, Wakefield J (1976) Subjective ratings of Cattell’s 16 personality factors. J Pers Assess 40(3):301–305

    Article  Google Scholar 

  14. He Y, Yan Y, Xu Q (2019) Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression. Int J Electr Power Energy Syst 113:515–527

    Article  Google Scholar 

  15. Horwitz EK, Horwitz MB, Cope J (1986) Foreign language classroom anxiety. The Modern Language of Journal 70(2):125–132

    Article  Google Scholar 

  16. Jigang C (2004) ESP and the direction of China's college English teaching. Foreign Language World 2(003)

  17. Maclmtyre PID, Gardner C (1993) The subtle effects of language anxiety on cognitive processing in the second language. Lang Learn 44:283–305

    Article  Google Scholar 

  18. Marina OA, Rajprasit K (2014) Investigating the impact of personality factors on perceived communication mobility of non-native English-speaking Thai professionals in international companies. PASAA: Journal of Language Teaching and Learning in Thailand 47:61–94

    Google Scholar 

  19. Mehryar AH (1972) Personality patterns of Iranian boys and girls on Cattell's 16 personality factor test. British Journal of Social and Clinical Psychology 11(3):257–264

    Article  Google Scholar 

  20. Mershon B, Gorsuch RL (1988) Number of factors in the personality sphere: does increase in factors increase predictability of real-life criteria? J Pers Soc Psychol 55(4):675–680

    Article  Google Scholar 

  21. Mikhnenko G, Absaliamova Y (2018) The formation of intellectual mobility of engineering students through integration of foreign language education and professional training. Advanced Education 9:33–38

    Article  Google Scholar 

  22. Noller P, Law H, Comrey AL (1987) Cattell, Comrey, and Eysenck personality factors compared: more evidence for the five robust factors? J Pers Soc Psychol 53(4):775–782

    Article  Google Scholar 

  23. Qiao QS, Wang HT, Wang ZY (2011) CET4 passing rate analysis based on fuzzy decision tree induction and active learning. In: International conference on machine learning and cybernetics, vol 1, pp 209–214

  24. Rimfeld K, Kovas Y, Dale PS (2016) True grit and genetics: predicting academic achievement from personality. J Pers Soc Psychol 111(5):780–789

    Article  Google Scholar 

  25. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674

    Article  MathSciNet  Google Scholar 

  26. Saville-Troike M (1984) What really matters in second language learning for academic achievement? TESOL Q 18(2):199–219

    Article  Google Scholar 

  27. Shaughnessy MF, Stockard J, Moore J (1994) Scores on the 16 personality factor questionnaire and success in college calculus. Psychol Rep 75(1):348–350

    Article  Google Scholar 

  28. Shen H, Li B, Tao M (2010) MSE-based transceiver designs for the MIMO interference channel[J]. IEEE Trans Wirel Commun 9(11):3480–3489

    Article  Google Scholar 

  29. Tempelaar DT, Gijselaers WH (2007) A structural equation model analyzing the relationship of student achievement motivations and personality factors in a range of academic subject-matter areas. Contemp Educ Psychol 32(1):105–131

    Article  Google Scholar 

  30. Vilaça M, Macedo E, Tafidis P, Coelho MC (2019) Multinomial logistic regression for prediction of vulnerable road users risk injuries based on spatial and temporal assessment. International Journal for Consumer & Product Safety 26:379–390

    Google Scholar 

  31. Xin W (2013) Improved Scheme of CET4 Test. Informatics and Management Science IV:739–745

    Google Scholar 

  32. Zabihi R (2011) Personality in English language proficiency and achievement[J]. Wilolud Journal 4(1):1–6

    Google Scholar 

  33. Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC press

  34. Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. In: NESUG proceedings: health care and life sciences, vol 19, p 67

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Acknowledgments

This work was supported by the Shaanxi Province Education Science “13th Five-Year” Planning Project of China (Grant No. SGH17H003).

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Authors and Affiliations

  1. School of Foreign Studies, Xi’an Jiaotong University, Xi’an, Shaanxi Province, 710049, People’s Republic of China

    Yuping Lin

  2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Province, 710049, People’s Republic of China

    Panpan Song & Hong Long

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  1. Yuping Lin
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  2. Panpan Song
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Correspondence to Yuping Lin.

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Lin, Y., Song, P. & Long, H. Accurate language achievement prediction method based on multi-model ensemble using personality factors. Multimed Tools Appl 80, 17415–17428 (2021). https://doi.org/10.1007/s11042-020-09297-4

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  • DOI: https://doi.org/10.1007/s11042-020-09297-4

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