Motivating students with different needs to learn Chinese in a mixed-background classroom by robot-assisted learning

Ka Yan FUNG, Kwong Chiu FUNG, Tze Leung Rick LUI, Kuen Fung Kenneth SIN, Lik-Hang LEE, Huamin QU, Shenghui SONG

Research output: Contribution to journalArticlespeer-review

Abstract

Mastering the visually complex characters of the Chinese language poses significant challenges for students. The situation is even worse in Hong Kong, where students with different backgrounds, including students with/without dyslexia and non-Chinese speaking students (NCS), are placed in the same class. Interactive design has been proven effective in enhancing students' learning performance and engagement. However, developing a learning tool for students with diverse backgrounds is challenging. This study proposes a robot-assisted Chinese learning system (RACLS) for those with diverse backgrounds and investigates its impact on learning motivation by a comparison study. In particular, 39 students participate in a five-day robot-led training program, while another 39 students received traditional teacher-led training. The comparison results show that RACLS can enhance the emotional engagement of students with dyslexia and strengthen the behavioural engagement of students without dyslexia. Interestingly, the learning motivation of NCS students in the experimental and control groups is enhanced similarly. Copyright © 2025 IEEE.
Original languageEnglish
JournalIEEE Transactions on Learning Technologies
Early online dateMar 2025
DOIs
Publication statusE-pub ahead of print - Mar 2025

Citation

Fung, K.-Y., Fung, K.-C., Lui, T. L. R., Sin, K.-F., Lee, L.-H., Qu, H., & Song, S. (2025). Motivating students with different needs to learn Chinese in a mixed-background classroom by robot-assisted learning. IEEE Transactions on Learning Technologies. Advance online publication. https://doi.org/10.1109/TLT.2025.3551256

Keywords

  • Robot-assisted language learning (RALL)
  • Dyslexia
  • Non-Chinese Speaking (NCS) students
  • Self-determine theory (SDT)
  • Learning engagement
  • Learning motivation
  • Cognitive Load Theory (CLT)
  • ARCS Model
  • Universal Design for Learning (UDL)

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