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Automatically Difficulty Grading Method Based on Knowledge Tree

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

Abstract

The aim of the current study is to propose a model, which can automatically grade difficulty for a question from “instruction system” question bank. The system mainly uses 4 attributes with 26 features based on principal component analysis, which are employed to be input of the Automatically Difficulty Grading Model (ADGM). A knowledge tree model and a machine learning algorithm are utilized as important parts for the classification module. The experimental dataset “instruction system” question bank is based on our built “Principles of Computer Organization” online education system, the accuracy result of difficulty classification could be 77.43% which is much higher than the accuracy of random guess 50%.

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Notes

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Acknowledgments

The authors would like to thank Master Weilai Liu for providing the help of the ADGM model. The authors would also like to thank the anonymous referees for their valuable comments and helpful suggestions. The work is supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China award (No. 20130031120028), Research Plan in Application Foundation and Advanced Technologies in Tianjin award (No. 14JCQNJC00700), Open Project of the State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences award under Grant No. CARCH201604.

Author information

Authors and Affiliations

  1. College of Computer and Control Engineering, Nankai University, Tianjin, 300-350, China

    Jin Zhang, Chengcheng Liu, Haoxiang Yang, Fan Feng & Xiaoli Gong

  2. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Haidian, China

    Fan Feng & Xiaoli Gong

  3. China Academy of Information and Communications Technology, Beijing, China

    Chengcheng Liu

Authors
  1. Jin Zhang
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  2. Chengcheng Liu
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  3. Haoxiang Yang
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  4. Fan Feng
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  5. Xiaoli Gong
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Corresponding author

Correspondence to Xiaoli Gong .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Zhang, J., Liu, C., Yang, H., Feng, F., Gong, X. (2017). Automatically Difficulty Grading Method Based on Knowledge Tree. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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