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An Academic Achievement Prediction Model Enhanced by Stacking Network

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Digital TV and Wireless Multimedia Communication (IFTC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1181))

Abstract

This article focuses on the use of data mining and machine learning in AI education to achieve better prediction accuracy of students’ academic achievement. So far, there are already many well-built gradient boosting machines for small data sets prediction, such as lightGBM, XGBoost, etc. Based on this, we presented and experimented a new method in a regression prediction. Our Stacking Network combines the traditional ensemble models with the idea of deep neural network. Compared with the original Stacking method, Stacking Network can infinitely increase the number of layers, making the effect of Stacking Network much higher than that of traditional Stacking. Simultaneously, compared with deep neural network, this Stacking Network inherits the advantages of the Boosting machines. We have applied this approach to achieve higher accuracy and better speed than the conventional Deep neural network. And also, we achieved a highest rank on the Middle School Grade Dataset provided by Shanghai Telecom Corporation.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Shaofeng Zhang, Meng Liu & Jingtao Zhang

Authors
  1. Shaofeng Zhang
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  2. Meng Liu
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  3. Jingtao Zhang
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Corresponding author

Correspondence to Shaofeng Zhang .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai University, Shanghai, China

    Ping An

  5. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

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Zhang, S., Liu, M., Zhang, J. (2020). An Academic Achievement Prediction Model Enhanced by Stacking Network. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_20

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  • DOI: https://doi.org/10.1007/978-981-15-3341-9_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3340-2

  • Online ISBN: 978-981-15-3341-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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