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A digital pen-based writing state recognition algorithm for student performance assessment

  • S.I.: Neural computing and intelligent education applications
  • Published:
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Abstract

Technology-enhanced learning is an irresistible trend in intelligent education. However, most digital pen-based studies focus on handwriting character recognition, writing behavior research are extremely scarce. In this work, we prototype an embedded digital pen aimed at classifying students’ writing behaviors. Utilizing state recognition, feature extraction and optimized k-means modeling method, we present a WSR (Writing State Recognition) algorithm. WSR can classify writing and short-writing indexes. One hundred and eighteen juniors participated in the algorithm validation. Experiment results show that writing behaviors are strongly correlated with the test scores. Our proposed WSR algorithm can help teachers grasp students’ writing status, assess performance and acquaint learning emotions. The digital pen-based assistant application can shed light on personalized teaching and also has great prospects in the future education.

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Funding

Guangdong Provincial Philosophy and Social Science Plan (GD23XXL10). 2022 Annual Planning Project of China Private Education Association (CANFZG22322).

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Authors and Affiliations

  1. School of Information Engineering, Guangzhou College of Commerce, Road Jiulong, Guangzhou, 518055, Guangdong, China

    Laiquan Han

  2. College of Humanities, Nanchang University, Xuefu Street, Nanchang, 330031, Jiangxi, China

    Bo Pan

  3. Department of School Mental Health, Minhang Institute of Education, Minhang Street, Shanghai, 456652, China

    Ying Chen

  4. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 518055, Guangdong, China

    Jianhua Tang

Authors
  1. Laiquan Han
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  2. Bo Pan
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  3. Ying Chen
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  4. Jianhua Tang
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Correspondence to Laiquan Han.

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Han, L., Pan, B., Chen, Y. et al. A digital pen-based writing state recognition algorithm for student performance assessment. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09955-w

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  • DOI: https://doi.org/10.1007/s00521-024-09955-w

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