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Using WPBL to Improve Engineering Undergraduates’ Computational Thinking Performance in Flipped Classroom

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Blended Learning : Lessons Learned and Ways Forward (ICBL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13978))

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Abstract

With the continuous development of artificial intelligence, more and more attention has been paid to improving students’ computational thinking capability in K-12 and higher education. Lots of research has shown that there is a close relationship between engineering education and computational thinking. Engineering thinking and computational thinking intertwine to some extent. The cultivation of engineering students’ computational thinking ability could be integrated into engineering content learning. However, in current research, there is limited research on how to improve engineering students’ computational thinking in an integrated way. Therefore, this study adopted Web Problem-Based Learning (WPBL) in a flipped engineering undergraduate course with the aim of enhancing students’ computational thinking performance in an active way of engineering content learning. A total of 110 third-year undergraduates participated in this quasi-experimental study. The 55 students the experimental group who learned via WPBL in flipped classroom performed significantly better than the other 55 students in the control group who learned via Q and A, mini-lectures and data analysis as in-class learning activities in flipped classroom. Specifically, the experimental group scored significantly higher on the Abstraction and Algorithm Thinking dimensions.

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Author information

Authors and Affiliations

  1. Central China Normal University, Wuhan, 430079, China

    Xiangjun Chen, Taotao Long, Luwei Cheng & Xiaojing Gan

  2. Wuhan University of Technology, Wuhan, 430070, China

    Xiaomeng Zhu

Authors
  1. Xiangjun Chen
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  2. Taotao Long
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  3. Luwei Cheng
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  4. Xiaojing Gan
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  5. Xiaomeng Zhu
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Corresponding author

Correspondence to Xiaomeng Zhu .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Chen Li

  2. Hong Kong Metropolitan University, Kowloon, Hong Kong

    Simon K. S. Cheung

  3. Hong Kong Metropolitan University, Kowloon, Hong Kong

    Fu Lee Wang

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

    Angel Lu

  5. HKCT Institute of Higher Education, Sha Tin District, Hong Kong

    Lam For Kwok

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chen, X., Long, T., Cheng, L., Gan, X., Zhu, X. (2023). Using WPBL to Improve Engineering Undergraduates’ Computational Thinking Performance in Flipped Classroom. In: Li, C., Cheung, S.K.S., Wang, F.L., Lu, A., Kwok, L.F. (eds) Blended Learning : Lessons Learned and Ways Forward . ICBL 2023. Lecture Notes in Computer Science, vol 13978. Springer, Cham. https://doi.org/10.1007/978-3-031-35731-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-35731-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35730-5

  • Online ISBN: 978-3-031-35731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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