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A Learning Early-Warning Model Based on Knowledge Points

  • Jiahe Zhai
  • Zhengzhou ZhuEmail author
  • Deqi Li
  • Nanxiong Huang
  • Kaiyue Zhang
  • Yuqi Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11528)

Abstract

Learning early-warning is one of the important ways to realize adaptive learning. Aiming at the problem of too large prediction granularity in learning early-warning, we divide student’s characters into three dimensions (knowledge, behavior and emotion). Secondly, we predict the student’s master degree of knowledge, based on the knowledge point. And then we realized learning early-warning model. In the model, we take 60 points as the learning early-warning standard, and take RF and GDBT as base classifiers, and give the strategy of selecting the basic model. The experiment shows that the prediction of knowledge mastery of the model and the real data Pearson correlation coefficient can reach 0.904279, and the prediction accuracy of the model below the early-warning line can reach 76%.

Keywords

Learning early-warning Emotion Type of question Knowledge points 

Notes

Acknowledgments

This paper was supported by National Key Research and Development Program of China (Grant No. 2017YFB1402400), Ministry of Education “Tiancheng Huizhi” Innovation Promotes Education Fund (Grant No. 2018B01004), National Natural Science Foundation of China (Grant No. 61402020), and CERNET Innovation Project (Grant No. NGII20170501).

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiahe Zhai
    • 1
  • Zhengzhou Zhu
    • 1
    Email author
  • Deqi Li
    • 1
  • Nanxiong Huang
    • 1
  • Kaiyue Zhang
    • 1
  • Yuqi Huang
    • 1
  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingPeople’s Republic of China

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