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Analysis and Application of Computer Modeling for MOOC

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

Abstract

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.

Keywords

Mu class Logistic regression Statistics SPSS 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.ShenYang City UniversityShenyangChina

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