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A Correlative Study of the Influence of Higher Vocational Students’ Learning Behavior on English Effective Learning

Volume 14, Number 5, May 2018, pp. 937-944
DOI: 10.23940/ijpe.18.05.p12.937944

Lei Chena, Xia Liua, and Qinghui Zhub

aSanya Aviation and Tourism College, Sanya, 572000, China
bCollege of Foreign Languages, Hainan University, Haikou, 570228, China

(Submitted on January 18, 2018; Revised on March 5, 2018; Accepted on April 15, 2018)

Abstract:

This study aims to explore the correlation between learning behavior and English effective learning. 1,758 answers to a questionnaire designed from the perspective of learning behavior are analyzed. The influence of English effective learning is summarized as constructive learning and destructive learning. Using SPSS to analyze the data and model a construction, we study the correlation between learning behavior and the main influencing factors, including constructive learning, destructive learning, mutual influence, learning burnout and employment pressure. The structural model results show that the alpha coefficients are above 0.6, and the corresponding variables load factor values are above 0.3, which proves that the questionnaire is valid and reliable. Correlation analysis is used to explore the impact of the major variables, indicating that significant correlations exist among the major variables. The regression analysis shows that constructive learning can significantly predict the learning behavior, while the destructive behavior can significantly negatively predict the learning behavior. The mutual influence cannot significantly predict the learning behavior. Through a structural equation model fitting analysis, the influence of classmates can significantly predict the learning behavior and employment pressure can significantly negatively predict the learning burnout. Furthermore, learning behavior plays an intermediary role in constructive learning, destructive learning and learning burnout. This study may provide reference for higher vocational English teaching reform from the data analysis.

 

References: 27

    1. A. Lisnianski and G. Levitin. Multi-state system reliability: Assessment, optimization and application. New York, NY, USA: World Scientific Publishing Co Pte Ltd., 2003.
    2. A. M. Rushdi. Reliability of k-out-of-n systems. In: Misra KB, editor. New trends in system reliability evaluation, 1993; vol. 16, pp. 185-227.
    3. A. M. Rushdi. Utilization of symmetric switching functions in the computation of k-out-of-n system reliability. Microelectronics and Reliability, 1986: vol. 26, pp. 973-987.
    4. B. Nailwal and S. B. Singh. Performance evaluation and reliability analysis of a complex system with three possibilities in repair with the application of copula. International Journal of Reliability and Applications. 2011; Vol. 12, no. 1, pp. 15-39.
    5. C. Simon and P. Weber. Evidential networks for reliability analysis and performance evaluation of systems with imprecise knowledge. IEEE Transactions on Reliability, 2009; Vol. 58, no. 1, pp. 69–87.
    6. C.-H. Cheng and D.-L. Mon. Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets and Systems, 1993; Vol. 56, no.1, pp.29–35.
    7. E. B. Jamkhaneh. An evaluation of the systems reliability using fuzzy lifetime distributions. Journal of Applied Mathematics, 2011; Vol. 8, pp. 73-80.
    8. G. Arulmozhi. Exact equation and an algorithm for reliability evaluation of k-out-of-n: G system. Reliability Engineering and system safety, 2002; Vol. 78(2), pp. 87–91.
    9. G. Levitin. The universal generating function in reliability analysis and optimization. Berlin, Germany: Springer-Verlag, 2005.
    10. I. Ushakov. A universal generating function. Soviet Journal of Computer and Systems Sciences, 1986; Vol. 5, pp. 118-129.
    11. J. K. Vaurio. Unavailability equations for k-out-of-n systems. Reliability Engineering and system safety, 2011; Vol. 96(2), pp. 350–352.
    12. J. S. Wu and R. J. Chen. An algorithm for computing the reliability of a weighted-k-out-of-n system. IEEE Transactions on Reliability. 1994; Vol. 43, pp. 327-328.
    13. Jin H, Lundteigen MA, Rausand M. New PFH-formulas for k-out-of-n: F-systems. Reliability Engineering and system safety, 2013; Vol. 111, pp. 112–118.
    14. K. B. Misra. Reliability analysis and prediction: A methodology oriented treatment. Elsevier, Amsterdam, 1992.
    15. K. Meenakshi and S. B. Singh. Availability assessment of multi-state system by hybrid universal generating function and probability intervals. International Journal of Performability Engineering, 2016; Vol. 12, no. 4, pp. 321-339.
    16. K. S. Bohra and S. B. Singh. Evaluating fuzzy system reliability using intuitionistic fuzzy Rayleigh lifetime distribution. Mathematics in Engineering, Science and Aerospace.2015; Vol. 6 (2), pp. 245-56.
    17. M. Ram and S. B. Singh. Availability, MTTF and cost analysis of complex system under preemptive-repeat repair discipline using Gumbel-Hougaard family copula. International Journal of Quality & Reliability management, 2010; Vol. 27, no. 3, pp. 576-5 95
    18. M. Sallak, C. Simon, and J.F. Aubry. A fuzzy probabilistic approach for determining safety integrity level. IEEE Transactions on Fuzzy Systems, 2008; Vol. 16, no. 1, pp. 239–248.
    19. P. Kumar and S. B. Singh. Fuzzy system reliability using intuitionistic fuzzy Weibull lifetime distribution. International Journal of Reliability and Application, 2015; Vol. 16 (1), pp. 15-26.
    20. S. Chaube and S. B. Singh. Fuzzy Reliability evaluation of two-stage fuzzy weighted-k-out-of-n systems having common components. Engineering and Automation Problems, 2014; Vol. 4, pp. 112-117.
    21. S. Destercke and M. Sallak. An extension of universal generating function in multi-state systems considering epistemic uncertainty. IEEE Transactions on Reliability, 2013; Vol. 62, no. 2, pp. 504-514.
    22. S. Negi and S. B. Singh. Reliability analysis of non-repairable complex system with weighted subsystems connected in series. Applied Mathematics and Computation, 2015; Vol. 261, pp.79-89.
    23. S. V. Amari and L. Xing. Reliability analysis of k-out-of-n systems with phased mission requirements. International Journal of Performability Engineering, 2011; Vol. 7, no. 6, pp. 604-609.
    24. T. Aven. Interpretations of alternative uncertainty representations in a reliability and risk analysis context. Reliability Engineering and system safety, 2011; Vol. 96, pp. 353–360.
    25. W. Kuo and M.J. Zuo. Optimal reliability modeling. Wiley, New York, Chapter 7, 258-264, 2003.
    26. W. Li and M. J. Zuo. Reliability evaluation of multi-state weighted k-out-of-n systems. Reliability Engineering and System Safety, 2008; Vol. 93, pp. 160-167.
    27. W. Li., M. J. Zuo. and Y. Ding. Optimal design of binary weighted k-out-of-n systems. International journal of Reliability, Quality and Safety Engineering, 2008; Vol. 15, no. 05, pp. 425-440.

       

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