A Kinect- and Game-Based Interactive Learning System
 
 
 
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No. 1, Nan-Tai Street, Yungkang Dist., Tainan, Taiwan
 
2
Tainan, Taiwan
 
 
Online publication date: 2017-08-01
 
 
Publication date: 2017-08-01
 
 
Corresponding author
Yi-Hsing Chang   

No. 1, Nan-Tai Street, Yungkang Dist., Tainan, Taiwan
 
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):4897-4914
 
KEYWORDS
ABSTRACT
This study combined the attention–relevance–confidence–satisfaction (ARCS) motivation model with a game design model to develop a Kinect- and game-based interactive learning system to enhance learning motivation and effect. In this system, game characteristics incorporated into the learning procedure through a game model allowed the learner to have fun while learning, and it stimulated their interest in the learning activities. A Kinect-based somatosensory interface was adopted to enable the learner to control virtual characters by using their physical movements. The learning objective investigated in this study was to learn about various zoo animals. Sixty adults aged 20–25 years were recruited as experiment participants, divided equally between the experimental and control groups. Learning effect was analyzed by using a t test to compare the participants’ performance in tests administered before and after one hour of learning. Learning motivation was investigated using a questionnaire in which the four elements of the ARCS model were adopted as four dimensions of inquiry. Compared with the control group, the experimental group achieved a larger test score improvement. Analyzing the questionnaire results confirmed that the learners had significantly increased motivation across all four ARCS dimensions. Finally, learners gave positive evaluations of the developed learning system.
 
REFERENCES (43)
1.
Alhazbi, S. (2015). ARCS-based tactics to improve students’ motivation in computer programming course. 10th International Conference on Computer Science and Education, ICCSE 2015, 317-321, September 9.
 
2.
Chang, Y.H., Lin, Y. K., Fang, R. J, & Lu, Y. T. (2017). A Situated Cultural Festival Learning System Based on Motion Sensing. Eurasia Journal of Mathematics, Science & Technology Education, 13(3), 571-588.
 
3.
Chao, K. J., Huang, H. W., Fang, W. C., & Chen, N. S. (2013). Embodied play to learn: exploring Kinect-facilitated memory performance. British Journal of Education technology, 44(5), E151-E155.
 
4.
Chen, T. S., Chiu, P. S., Huang, Y. M., & Chang, C. S. (2011). A study of learners’ attitudes using TAM in a context-aware mobile learning environment. International Journal of Mobile Learning and Organization, 5(2), 144-158.
 
5.
Cheong, S. N., Yap, W. J., Logeswaran, R., & Chai, I. (2012). Design and development of kinect-based technology-enhanced teaching classroom. Lecture Notes in Electrical Engineering, 181, 179-186.
 
6.
Cheung, K. K. F., Jong, M.S. Y., Lee, F. L., Lee, J. H. M., Luk, E. T. H., Shang, J., & Wong, M. K. H. (2008). FARMTASIA: an online game-based learning environment based on the VISOLE pedagogy. Virtual Reality, 12(1), 17-25.
 
7.
Chye, C., & Nakajima, T. (2012). Game based approach to learn martial arts for beginners. 18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, 482-485, doi: 10.1109/RTCSA.2012.37.
 
8.
Doyle, J., Caprani, N., & Bond, R. (2015). Older adults’ attitudes to selfmanagement of health and wellness through smart home data. In IEEE Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare, 129–136.
 
9.
Ejupi, A., Gschwind, Y. J., Valenzuela, T., Lord, S. R., & Delbaere, K. (2015). A kinect and inertial sensor-based system for the self-assessment of fall risk: a home-based study in older people. Human–Computer Interaction, 1–33.
 
10.
Erdoan, H., & Ekenel, H. K. (2015). Game design for physical therapy and rehabilitation using Kinect. Medical Technologies National Conference, TIPTEKNO, October 15 - October 18.
 
11.
Giannakas, F., & Gritzalis, S. (2015). CyberAware: A mobile game-based app for cybersecurity education and awareness. Interactive Mobile Communication Technologies and Learning (IMCL), 2015 International Conference on, 19(20), 54-58.
 
12.
Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation and learning, simulation & gaming: An Interdisciplinary Journal Practice and Research. Simulation Gaming December, 33(4), 441-467.
 
13.
Ghergulescu, I., & Muntean, C. H. (2014). Motivation monitoring and assessment extension for Input-Process-Outcome game model. International Journal of Game-Based Learning (IJGBL), 4(2), 15-35.
 
14.
Hamzah, W. M. A. F. W., Ali, N. H., Saman, M. Y. M., Yusoff, M. H., & Yacob, A. (2015). Influence of gamification on students’ motivation in using E-learning applications based on the motivational design model. International Journal of Emerging Technologies in Learning, 10(2), 30-34.
 
15.
Hsiao, H. S., & Chen, J. C. (2016). Using a gesture interactive game-based learning approach to improve preschool children’s learning performance and motor skills. Computers &Education, 151-162.
 
16.
Höysniemi, J., Hämäläinen, P., Turkki, L., & Rouvi, T. (2005). Children’s intuitive gestures in vision-based action games. Communications of the ACM, 48(1), 44-50.
 
17.
Kaneko, K., Saito, K., Nohara, N., Kudo, E., & Yamada, M. (2015). A game-based learning environment using the ARCS model at a university library. Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on, 12(6).
 
18.
Keller, J. M. (2000). How to integrate learner motivation planning into lesson planning: The ARCS model approach. VII Semanario, Santiago, Cuba.
 
19.
Kinect for Windows (2016). http://kinectforwindows.org/ [Retrieved by 2016/1/10].
 
20.
Lee, L. C., & Hao, K. C. (2015). Designing and evaluating digital game-based learning with the arcs motivation model, humor, and animation. International Journal of Technology and Human Interaction, 11(2), 80-95.
 
21.
Lee, W. J., Huang, C. W., Wu, C. J., Huang, S. T., & Chen, G. D. (2012). The effects of using embodied interactions to improve learning performance. 2012 IEEE 12th international conference on advanced learning technologies (ICALT), 557-559, July 4-6.
 
22.
Li, P. L., Wang, J. C., Wu, C. H., & Chen, C. H. (2014). The design of motion-sensing computer games applying on the physical education curriculum for students with special needs. Special Education & Assistive Technology, 10, 41-51.
 
23.
Lu, S. J., Liu, Y. C., Chuang, Y. C., & Peng, C. P. (2012). A research of applying physically interactive games in the elementary Minan dialect curriculum and instruction. Curriculum & Instruction Quarterly, 15(2), 169-191.
 
24.
Ofli, F., Kurillo, G., Obdrzalek, S., Bajcsy, R., Jimison, H. B., & Pavel, M. (2016). Design and Evaluation of an Interactive Exercise Coaching System for Older Adults. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 20(1), 201-212.
 
25.
Papastergiou, M. (2009). Exploring the potential of computer and video games for health and physical education: A literature review. Computers & Education, 53(3), 603-622.
 
26.
Peng, J., Liu, R., & Liu, G. (2016). The Design and Implementation of Task-Based Learning Activities in 3D Virtual Environment. Proceedings - 5th International Conference on Educational Innovation through Technology, 20-25.
 
27.
Prensky, M. (2001). Digital Game-Based Learning, McGraw-Hill, New York.
 
28.
Qian, X. (2014). Construction and application of an educational game based on the ARCS model. World Transactions on Engineering and Technology Education, 12(2), 236-241.
 
29.
Sheu, F. R., & Chen, N. S. (2014). Taking a signal: a review of gesture-based computing research in education. Computers & Education, 78, 268-277.
 
30.
Taipei Zoo. (2016). http://www.zoo.gov.tw/, Retrieved by [2016/5/15].
 
31.
Too, M. S. M., Ong, P. T., Lau, S. H., Chang, R. K. Y., & Sim, K. S. (2016). Kinect-based framework for enhanced learning of disabled students. International Conference on Robotics, Automation and Sciences, ICORAS 2016, November 5, - November 6.
 
32.
Tsai, C. H, Kuo, Y. H., Chu, K. C., & Yen, J. C. (2015). Development and evaluation of game-based learning system using the Microsoft Kinect sensor. International Journal of Distributed Sensor Networks, 11(7). doi: 10.1155/2015/498560.
 
33.
Tsai, C. H., & Yen, J. C. (2016). Effect of an Equivalent Fractions Digital Game on the Learning Outcome, Motivation, and Flow Types Among Elementary School Students. 2016 International Conference on Educational Innovation through Technology, 70-75.
 
34.
Tsai, F. H., Tsai, C. C., & Lin, K. Y. (2015). The evaluation of different gaming modes and feedback types on game-based formative assessment in an online learning environment. Computers & Education, 81, 259-269.
 
35.
Vos, N., Meijden, H. V., & Denessen, E. (2011). Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers & Education, 56, 127-137.
 
36.
Wu, P. F., Huang, M. J., & Chang, N. W. (2013). The learning experience of fine art by somatosensory game device. In IEEE 20135th international conference on service science and innovation (ICSSI), 104-114.
 
37.
Yang. S. Y., Lin, S. J., & Tsai, C. Y. (2014). Comparison of fundamental movement skills among young children with different gender, age, and BMI. Sports & Exercise Research, 16(3), 287-296.
 
38.
Yang, Y. H., Xu, W., Zhang, H., Zhang, J. P. & Xu, M. L. (2014). The application of KINECT motion sensing technology in game-oriented study. International Journal of Emerging Technologies in Learning, 9(2), 59-63.
 
39.
Yurdaarmagan, B., Melek, C. G., Cikrikcili, O., Salman, Y. B., & Cheng, H. I. (2015). The effects of digital game-based learning on performance and motivation for high school students. ICIC Express Letters, 9(5), 1465-1469.
 
40.
Zhang, W. (2017). Design a civil engineering micro-lecture platform based on the ARCS model perspective. International Journal of Emerging Technologies in Learning, 12(1), 107-118.
 
41.
Zhang, Z., Conly, C., & Athitsos, V. (2015). A survey on vision-based fall detection. Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, doi: 10.1145/2769493.2769540.
 
42.
Zhao, W., Lun, R., Espy, D. D., & Reinthal, M. A. (2014). Realtime motion assessment for rehabilitation exercises: Integration of kinematic modeling with fuzzy inference. Journal of Artificial Intelligence and Soft Computing Research, 4(4), 267–285.
 
43.
Zhao, W., & Lun, R. (2016). A Kinect-based system for promoting healthier living at home. IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, October 9 - October 12.
 
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