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A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

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Abstract

We propose a simple but effective method to recommend exercises with high quality and diversity for students. Our method is made up of three key components: (1) candidate generation module; (2) diversity-promoting module; and (3) scope restriction module. The proposed method improves the overall recommendation performance in terms of recall, and increases the diversity of the recommended candidates by 0.81% compared to the baselines.

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Notes

  1. 1.

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Acknowledgements

This work was supported in part by National Key R &D Program of China, under Grant No. 2020AAA0104500; in part by Beijing Nova Program (Z201100006820068) from Beijing Municipal Science & Technology Commission and in part by NFSC under Grant No. 61877029.

Author information

Authors and Affiliations

  1. TAL Education Group, Beijing, China

    Shuyan Huang, Qiongqiong Liu, Jiahao Chen & Zitao Liu

  2. The University of Memphis, Memphis, TN, USA

    Xiangen Hu

  3. Guangdong Institute of Smart Education, Jinan University, Guangzhou, China

    Zitao Liu & Weiqi Luo

Authors
  1. Shuyan Huang
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  2. Qiongqiong Liu
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  3. Jiahao Chen
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  4. Xiangen Hu
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  5. Zitao Liu
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  6. Weiqi Luo
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Corresponding author

Correspondence to Zitao Liu .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Huang, S., Liu, Q., Chen, J., Hu, X., Liu, Z., Luo, W. (2022). A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_36

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_36

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