Advertisement

Precise Decision Algorithm for Difficult Students in Colleges and Universities Based on Big Data Analysis

  • Huijie QuEmail author
Conference paper
  • 28 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1147)

Abstract

Aiming at the problem of low accuracy and long time in traditional methods, this paper proposes a new method for determining difficult students in Colleges and Universities based on fuzzy optimal partition. This paper analyses the difficult students in Colleges and universities, uses the goal of enhancing the optimization performance to select the characteristics of the difficult students in Colleges and universities, analyses the characteristics selection and obtains the subset of the characteristics of the difficult students according to the Fisher ratio of the characteristic attributes of the difficult students in Colleges and universities. On the basis of improving the objective function, it constructs the fuzzy optimum corresponding to the definition index. Divide the decision algorithm to solve the optimal partition matrix and complete the precise judgement of the difficult students. The experimental results show that the proposed algorithm has a higher accuracy rate and a shorter time in the accurate judgment of college students with difficulties.

Keywords

Big data Difficult students in colleges Decision algorithm 

References

  1. 1.
    Ji, X., Wang, Y., Tong, X., et al.: Video shot segmentation algorithm based on gist feature and conditional decision. J. China Acad. Electron. Inf. Technol. 2, 34–56 (2018)Google Scholar
  2. 2.
    Yang, J., Zhi, N.: Stability analysis of Bi-DC/DC converter with constant power loads. J. Power Supply 15(6), 10.13234 (2017)Google Scholar
  3. 3.
    Tan, X., Xuan, T., Zhang, P.: Load identification and classification in non-intrusive load monitoring system based on data stream. Chin. J. Power Sources 40(5), 1110–1112 (2016)Google Scholar
  4. 4.
    Li, S., Yang, Z., Zhao, S., et al.: Global early paleozoic orogens (II): subduction-accretionary-type orogeny. J. Jilin Univ. Earth Sci. Ed. 46(4), 968–1004 (2016)Google Scholar
  5. 5.
    Speer, N., McFaul, M., Mohatt, D.: Nourishing students’ mental health in a difficult economy. Chronicle High. Educ. 55(36), A25–A26 (2009)Google Scholar
  6. 6.
    Barros, S., Claro, H.G.: The teaching-learning process in mental health: the student’s perspective about psychosocial rehabilitation and citizenship. Revista Da Escola de Enfermagem Da USP 45(3), 700–707 (2011)CrossRefGoogle Scholar
  7. 7.
    Clothey, R.: Educating global citizens in colleges and universities: challenges and opportunities. Rev. High. Educ. 34(3), 508–510 (2011)CrossRefGoogle Scholar
  8. 8.
    Williams, B.C.: Challenges and opportunities for collaboration in teacher education programs. J. Action Teach. Educ. 19(06), 89–96 (1997)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Xu, Y.: Access to higher education for rural-poor students in China. Educ. Res. Policy Pract. 9(3), 193–209 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Shen, H.: Access to higher education for disadvantaged groups in China. Chin. Educ. Soc. 37(1), 54–71 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mathematics and Information Science DepartmentGuangxi College of EducationNanningChina

Personalised recommendations