Analysis and Application of Computer Modeling for MOOC

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1147)


This paper investigates the reasons for the relatively low proportion of MOC learners in higher education. After analyzing the existing data, it was found that the number of MOOCs has been increasing in the past 1–2 years, but the number of learners has not increased year-on-year, and the proportion of total students in the school is low. The study analyzes the factors that may affect the choice of MOOCs by analyzing the learners themselves, and uses Logistic models to model the influencing factors and the results of MOOCs. Based on the analysis of the test results, it is concluded that the selection of the types of variables in the equation should be determined according to the best overall fit of the equation. This paper uses the known form of the equation to classify and filter the possibility of students choosing MOOC. Using this result, the teaching management department can target the non-selected students among the high-interest groups in MOOCs in a targeted manner, so as to increase the proportion of MOOC learners in the students and fully reflect the advantages of MOOC.


Mu class Logistic regression Statistics SPSS 


  1. 1.
    Liu, G., Li, J., Liang, Z.: Thoughts on and countermeasures for teaching innovation in colleges and universities in the “Internet +” era. China High. Educ. Res. (2), 93–98 (2017)Google Scholar
  2. 2.
    Fan, L.: The number of MOOCs in China has ranked first in the world. Wenhui Bao, (A2), 16 January 2018 (2018)Google Scholar
  3. 3.
    Gu, S., Wang, C., Gu, C.: Influencing factors of psychological toughness of college student athletes based on hierarchical multinomial logit model. J. Beijing Sport Univ. (03) (2017)Google Scholar
  4. 4.
    Lin, L., Tan, Q.: Maximum entropy distribution algorithm and its application. Syst. Eng. Electron. 29(5), 820–822 (2007)Google Scholar
  5. 5.
    Wang, J.: Logistic Regression Model: Method and Application. Higher Education Press, Beijing (2001)Google Scholar
  6. 6.
    Li, H., Mai, J., Xiao, M.: Application of dumb variables in logistic regression model. Evid. Med. (01), 42–45 (2008)Google Scholar
  7. 7.
    Xu, R.: Screening of logistic regression variables and selection of regression methods. Chin. J. Evid. Med. (11), 1360–1364 (2016)Google Scholar
  8. 8.
    Zhang, W.: Basic Course of SPSS Statistical Analysis, 2nd edn. Higher Education Press, Beijing (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.ShenYang City UniversityShenyangChina

Personalised recommendations