Learning Outcome: Do the students get improved in their dancing skills?
Arousing Interest: Does the system motivate the students more eager to learn?
Comparison with the Traditional Self-Learning Method: Does our proposed system outperform the “watching video” approach in learning dance?
4.2.1 Learning Outcome Achieved by Students The achievements of learning outcome were assessed by measuring the change of skills on specific dance motions while learning through our system.
The four subjects in the experiment group were told to learn some dance moves with our system (see Fig. 8). Each of them learned three dance moves and spent 15 minutes for each move. The three moves are about 2-seconds long Hip-Hop dance moves that were captured from an experienced dancer. Before the starting of the course, they were instructed to watch demonstrations of the dance moves to be learned. This allows subjects to get some basic knowledge about these moves. At the beginning of the course, baseline scores were derived by the similarity measurement between the subjects' motions and the template motions. After the baseline testing, the course started. During the course, the subjects can watch the demonstrations by the virtual teachers, practice their moves through our system and check out the errors happened in their movements through the feedback provided. After 15 minutes, the subjects did the post-training testing and the scores were obtained.
To assess the changes of skills before and after the course, the scores were analyzed by paired T-test. The mean of baseline scores is 40.58 and the standard deviation is 4.87. The mean of post-training scores is 51.41 and the standard deviation is 5.23. The -value is 0.000011598, the -value is 6.9833 and the degree of freedom is 11. Since , it shows that there is a significant difference before and after the training. As the mean after the training is higher than that before the training, it further showed that there is a significant improvement after training with our system.
4.2.2 Arousing Interest in the Participants Our system is evaluated to check whether it is able to motivate the students in the learning progress. This can be shown by the result of postcourse survey. Fig. 9 shows the questions in the postcourse survey and their results. It shows that our system is interesting and able to motivate subjects to learn. Provided that the subjects only have a little interest on dancing, it is already encouraging to make half of them felt that the course is definitely interesting. According to the extra comments, some subjects found that the scores in the feedback stimulated them to achieve better. Some suggested that it would be more exciting if they know the highest score achieved by other learners.
Another part of the postcourse survey is to find whether the system can provide them an easy way of learning. From the survey, none of them thinks that the dance motion is difficult to learn. The result is acceptable as all the subjects did not learn dancing before. By the way, the survey showed that most subjects are willing to recommend other people to try our system. Overall speaking, the subjects enjoy learning dance with our proposed system.
4.2.3 Comparison Evaluation To show that our system can overcome the shortage of self-learning approaches, four subjects in the control group were told to learn dance by self learning, i.e., no feedback is provided. Similar to the experiment group, the baseline and post-training scores were measured. In the control group, the subjects can learn dance moves by watching the demonstration and mimicking the movements without any external aid (see Fig. 10). In other words, no feedback is provided to them. The subjects in both groups have similar backgrounds and skills as shown in Table 3. A two sample T-test is carried out to compare the baseline scores of the experiment and control groups. The -value is 0.2116. Since , it shows that there is no significant difference between the baseline scores between the two groups.
The change between baseline and post-training scores were analyzed by the paired T-test. The mean of baseline scores is 37.08 and the standard deviation is 7.1536. The mean of post-training scores is 37.92 and the standard deviation is 5.02. The -value is 0.3374, the -value is 0.4309, and degree of freedom is 11. Since , the results show that there is no significant difference before and after training. This result does not support what people always assumed practice makes perfect. This is because the subjects may not get the key point to improve their skill during the learning progress.
The improvement of subjects in experiment group and control group were also analyzed by another T-test. The improvement is obtained by post-training score minus baseline score. The mean of improvement in experiment group is 10.83 and the standard deviation is 5.37. The mean of improvement in control group is 0.83 and the standard deviation is 6.70. The -value is 0.0012, the -value is 3.9178, and degree of freedom is 11. Since , it shows that there is a significant difference between improvements in two groups. As the mean of the improvement in experiment group is higher than that of control group, it further showed that there is a significant improvement after training with our system.
Compared with the result of the experiment group, our system is shown capable to guide the students to improve their skill in the learning process.
The four subjects in the control group also completed the postcourse survey. The result is shown in Fig. 11. The subjects in the control group faced more difficulty to learn the dance moves compared with the experiment group. However, the result in the experiment group is only slightly better than the control group for the remaining part of the survey. In the extra comments in the survey, some subjects thought that the motion capturing system made the course more interesting. It is unexpected, when we designed the evaluation and this should be avoided in the further studies.
Overall speaking, this evaluation result supports the hypothesis that our system can assist the students in dance training better than just watching demonstration without feedback. The feedback is proven useful in guiding students into the correct direction in the learning process, as well as stimulating them to learn more.
J.C.P. Chan, H. Leung, and J.K.T. Tang are with the Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong. E-mail: firstname.lastname@example.org,
T. Komura is with the School of Informatics, Edinburgh University, 10 Crichton Street, Edinburgh, EH8 9AB, United Kingdom.
Manuscript received 1 Dec. 2009; revised 26 May 2010; accepted 5 Aug. 2010; published online 17 Aug. 2010.
For information on obtaining reprints of this article, please send e-mail to: email@example.com, and reference IEEECS Log Number TLTSI-2009-12-0167.
Digital Object Identifier no. 10.1109/TLT.2010.27.
Jacky C.P. Chan received the BSc degree in computer science from the City University of Hong Kong in 2007. He is currently working toward the PhD degree in the Department of Computer Science at the City University of Hong Kong. His research interests include human motion analysis and human-computer interaction.
Howard Leung received the BEng degree in electrical engineering from McGill University, Montreal, Quebec, Canada, in 1998, and the MSc and the PhD degrees in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania, in 1999 and 2003, respectively. He is currently an assistant professor in the Department of Computer Science at the City University of Hong Kong. His current research interests include the pattern analysis of time series data. His projects include 3-D human motion analysis, intelligent tools for Chinese handwriting education, and Chinese calligraphic image analysis. He is the treasurer of the Hong Kong Web Society.
Jeff K.T. Tang received the BEng degree in computer engineering from the City University of Hong Kong in 2003 and the MSc degree in information technology from the Hong Kong University of Science and Technology in 2005. He is currently working toward the PhD degree in the Department of Computer Science at the City University of Hong Kong. His research interests include 3D human motion analysis, biomechanics, pattern recognition, Chinese handwriting education, and e-learning.
Taku Komura received the PhD, MSc, and BSc degrees in information science from the University of Tokyo in 2000, 1997, and 1995, respectively. He is currently a lecturer in the School of Informatics at University of Edinburgh. His research interests include human motion analysis and synthesis, physically based animation, and real-time computer graphics.