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The Jacobian Matrix-Based Learning Machine in Student

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Emerging Technologies for Education (SETE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10676))

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Abstract

The student learning performance is analyzed that we adopted the proposed Jacobian Matrix-based Learning Machine (JMLM). It is significant for establishing prediction machine learning model for student learning performance and these tool can help teacher to analyze the student data not difficult to analyze. Correct rate of our model is 87% and 86% better than traditional machine learning models.

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Acknowledgement

This paper was partly supported by the Ministry of Science and Technology, Taiwan, R.O.C, under 106-2511-S-019-003-, and 105-2218-E-008-014-.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan Ocean University, Keelung, Taiwan

    Yi-Zeng Hsieh

  2. Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan

    Mu-Chun Su

  3. Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Yu-Lin Jeng

Authors
  1. Yi-Zeng Hsieh
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  2. Mu-Chun Su
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  3. Yu-Lin Jeng
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Corresponding author

Correspondence to Yi-Zeng Hsieh .

Editor information

Editors and Affiliations

  1. National Taichung University of Science and Technology, Taichung City, Taiwan

    Tien-Chi Huang

  2. City University of Hong Kong, Kowloon, Hong Kong

    Rynson Lau

  3. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

    Yueh-Min Huang

  4. University of Caen Normandie, Caen, France

    Marc Spaniol

  5. City University of Hong Kong, Kowloon, Hong Kong

    Chun-Hung Yuen

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Hsieh, YZ., Su, MC., Jeng, YL. (2017). The Jacobian Matrix-Based Learning Machine in Student. In: Huang, TC., Lau, R., Huang, YM., Spaniol, M., Yuen, CH. (eds) Emerging Technologies for Education. SETE 2017. Lecture Notes in Computer Science(), vol 10676. Springer, Cham. https://doi.org/10.1007/978-3-319-71084-6_55

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  • DOI: https://doi.org/10.1007/978-3-319-71084-6_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71083-9

  • Online ISBN: 978-3-319-71084-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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