Applying Exergames in Classroom for Learning Chinese

Abstract

Applying computer games in second language learning (L2) has been proved to be very effective. The integration of learning styles and multiple intelligence theory may reduce students’ learning curve and enhance their learning strengths in L2 learning. However, the traditional games used in L2 are either too simple to be attractive to adult learners or too complicated to be adopted in the classroom. In this paper, to apply Cbodily-kinesthetic intelligence, we used motion sensors (i.e. Intel RealSense) to design a series of computer games for learning Chinese. It gives learners a new way to engage and interact so that it boosts learning efficacy. Based on the observation that gestures and speech show consistent or related meanings, engaging learners’ kinesthetic intelligence has shown to improve performance for foreign language learning, we designed several exergames that can practice Chinese characters and making sentences in classroom teaching and received positive feedback. The motion sensors hold promises to create kinesthetic games to help learners acquire second languages through constructions grounded in 3D space and body motion. The games can be designed using the Microlearning concept, which only covers small topics and fits classroom needs. The exergames can be extended to other foreign languages with little effort.

Keywords

• Microlearning
• Exergame
• Kinect
• Intel RealSense
• Kinesthetic

Supporting Institution

The City University of New York

References

1. Beaudin, J., Intille, S., Tapia, E., Rockinson, R. & Morris, M., (2007, Nov. 11). Context-sensitive microlearning of foreign language vocabulary on a mobile device. The 2007 European Conference on Ambient Intelligence, Darmstadt, Germany. http://doi.org/10.1007/978-3-540-76652-0_4
2. Chai, X., Li, G., Lin, Y., Xu, Z., Tang, Y., Chen, X. & Zhou, M. (2013, May 15). Sign language recognition and translation with Kinect. The 10th IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, China. https://doi.org/10.3390/s150100135
3. Darling-Hammond, L. (2010). Performance counts: Assessment systems that support high-quality learning. Council of Chief State School Officers Press.
4. Cerruti C. (2013). Building a functional multiple intelligences theory to advance educational neuroscience. Journal of Frontier in Psychology, 950(4),367-381.
5. Edge, D., Cheng, K., Whitney, M., Qian, Y., Yan, Z. & Soong, F. (2012, Sept. 18). Tip tap tones: Mobile micro training of Mandarin sounds. MobileHCI 2012 Conference on Human Computer Interaction with Mobile Devices and Services, San Francisco, California, USA. https://doi.org/10.1145/2371664.2371715
6. Edge, D., Fitchett, S., Whitney, M., & Landay, J. (2012, Sept. 20). MemReflex: Adaptive flashcards for mobile microlearning, MobileHCI 2012 Conference on Human Computer Interaction with Mobile Devices and Services. San Francisco, California, USA. https://doi.org/10.1145/2371574.2371641
7. Edge, D., Searle, E., Chiu, K., Zhao, J. & Landay, J. (2011, May 10). MicroMandarin: Mobile language learning in context, CHI 2011 Conference on Human Factors in Information Systems. Vancouver, BC, Canada. https://doi.org/10.1145/1978942.1979413
8. Erradi, A., Nahia, S. & Almerekhi, H. (2013, Jul. 15). Game-based micro-learning approach for language vocabulary acquisition using LingoSnacks, IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China. http://doi.org/10.1109/ICALT.2013.73

9. Gullberg, M. (2008). Handbook of cognitive linguistics and second language acquisition. Routledge Taylor & Francis Group.
10. Gameiro, J., Cardoso, T. & Rybarczyk, Y. (2014). Kinect-Sign, teaching sign language to “listeners” through a game. Procedia Technology, 17, 384–391.
11. Harvey, S., Strong, R. & Perini, M. (1997). Integrating learning styles and multiple intelligences. Educational Leadership, 55, 22-27.
12. Hug, T. (2005, May 6). Micro Learning and narration: Exploring possibilities of utilization of narrations and storytelling for the designing of "micro units" and didactical micro-learning arrangements. The 4th Media in Transition conference, Cambridge, MA, USA, http://web.mit.edu/comm-forum/mit4/papers/hug.pdf
13. Kendon, A. (1972). Some relationships between body motion and speech: Studies in dyadic communication. Pergamon Press Inc.
14. Leap Motion. https://www.leapmotion.com, last visit Oct. 2020.
15. Johnson, J. & Valente, A. (2009). Tactical language and culture training systems: using artificial intelligence to teach foreign languages and cultures. AI Magazine, 30(2), 72-74.
16. Logsdon, A. (2021). Bodily kinesthetic learning style and characteristics: Learning through physical hand and body movement. https://www.verywellfamily.com/kinesthetic-learner-characteristics-2162776
17. Mentira: http://www.mentira.org/the-game Last visit Sept. 24, 2021
18. Microsoft Kinect. http://www.xbox.com/en-US/kinect, last visit Oct. 20, 2020
19. Palmer, J. (2001). Fifty modern thinkers on education: From Piaget to the present day. Routledge Taylor & Francis Group.
20. Reinhardt, J. & Sykes, J. (2013). Language at play: Digital games in second and foreign language teaching and learning. Pearson Education.
21. Shepherd, J., Doe, R.L., Arnold, M., Cheek, N., Zhu, Y. & Tang, J. (2011, Mar. 24). Lost in the Middle Kingdom: A second language acquisition video game. The 49th Annual Southeast Regional Conference. Kennesaw, USA. https://doi.org/10.1145/2016039.2016114
22. Viberg, O. and Grönlund, Å. (2012, Oct. 10). Mobile assisted language learning: A literature review. 11th World Conference on Mobile and Contextual Learning. Helsinki, Finland. http://ceur-ws.org/Vol-955/papers/paper_8.pdf
23. Yang, H.D. (2015). Sign language recognition with the Kinect sensor based on conditional random fields. Sensors, 15(1). 135-147.
24. Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H. & Presti, P. (2011, Nov. 14), American Sign Language recognition with the Kinect. The International Conference on Multimodal Interfaces. Alicante, Spain. https://doi.org/10.1145/2070481.2070532
25. Zon: http://zon-labs.com/ last visited Oct. 14, 2020