IEEE Consumer Electronics Magazine - July 2018 - 25

interaction method reinforced their memory and enhanced the
learning enjoyment, which made them more involved in the
practice of dance movement. The instant feedback from the
PAC shown on the system interface helped users adjust their
corresponding joints to improve the correctness of their dance
learning. As for the satisfaction scale, the experimental group
was better than that in control group, meaning that PAC satisfied the users' needs in dance learning while the traditional
instruction video did not.

CONCLUSION
Just like the Kinect's motto, "You are the controller," PAC
makes it possible for every dancer to be his or her own
controller. It is the first thing that digitizes and evaluates the
Tujia hand-waving dance in 3-D data, combining the technology of the Kinect sensors with protection for the traditional
hand-waving dance. This article is among the first that focuses
on the automatic evaluation of students' dance performance
based on their captured movements. It proposes a novel
approach for dance movement data analysis evaluation of the
learner's performance qualitatively and quantitatively using the
method of game-based learning. Moreover, it introduces the
dance movements database for evaluation and protection of the
Tujia hand-waving dance. Future research can concentrate on
expanding the dance movements database to enlarge the application of the PAC to other motor skills learning, such as tai chi
and Chinese kung fu.

ACKNOWLEDGMENTS
This work was funded by the National Science and Technology plan project, Key Technology Research and Demonstration of Tujia music culture digital protection and display
(2015BAK03B03).

ABOUT THE AUTHORS
Wang Yang (wangyang910519@foxmail.com) is a doctor at
the Central China Normal University, Wuhan. Her research
focuses on dance learning analytics.
Liu Qingtang (liuqtang@mail.ccnu.edu.cn) is a professor at the Central China Normal University, Wuhan. His
research focuses on learning analytics.
He Haoyi (he_haoyi@163.com) is an instructor at the
Central China Normal University, Wuhan. His research
focuses on learning behavior analytics.
Yang Hairu (82393351@qq.com) is an instructor at the
Central China Normal University, Wuhan. His research
focuses on learning behavior analytics.
Yu Shufan (yushufan66@hotmail.com) is a master's
degree student at the Central China Normal University,
Wuhan. His research focuses on dance learning analytics.
Le Huixiao (554068550@qq.com) is a master's degree
student at the Central China Normal University, Wuhan. His
research focuses on dance learning analytics.
Yuan Yangyang (1223406222@qq.com) is a master's
degree student at the Central China Normal University,
Wuhan. His research focuses on dance learning analytics.

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http://en.cnki.com.cn/Article_en/CJFDTOTALBJTD201112007.htm http://en.cnki.com.cn/Article_en/CJFDTOTALBJTD201112007.htm http://www.rcampus.com/rubricshowc.cfm?code=PX72XA7&sp=true&nocache=1489486622280 http://www.rcampus.com/rubricshowc.cfm?code=PX72XA7&sp=true&nocache=1489486622280 https://www.rcampus.com/rubricshowc.cfm?code=WX72A9C&nocache=1481682108595 https://www.rcampus.com/rubricshowc.cfm?code=WX72A9C&nocache=1481682108595
Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - July 2018
