Modeling and verification of an intelligent tutoring system based on Petri net theory

Yu-Ying Wang; Ah-Fur Lai; Rong-Kuan Shen; Cheng-Ying Yang; Victor R.L. Shen; Ya-Hsuan Chu; Yu-Ying Wang; Ah-Fur Lai; Rong-Kuan Shen; Cheng-Ying Yang; Victor R.L. Shen; Ya-Hsuan Chu

doi:10.3934/mbe.2019250

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4947-4975. doi: 10.3934/mbe.2019250

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Modeling and verification of an intelligent tutoring system based on Petri net theory

1.
Department of Applied Foreign Languages, Jinwen University of Science and Technology, 99, Anzhong Rd., Xindian Dist., New Taipei City 23154, Taiwan
2.
Department of Computer Science, University of Taipei, 1, Ai-Guo West Road, Taipei City, 10048 Taiwan
3.
Department of Japanese Language and Literature, Shih Hsin University, #1 Lane17, Sec.1, Mu-Cha Rd., Taipei City, Taiwan
4.
Department of Computer Science and Information Engineering, National Taipei University, 151, University Rd., Sanxia District, New Taipei City, 237 Taiwan
5.
Department of Information Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung City 413, Taiwan

Received: 25 January 2019 Accepted: 24 March 2019 Published: 30 May 2019

According to the educational regulations in Taiwan, students are required to learn English when they are at the first grade of elementary school. However, not all the students have an appropriate environment to practice English, especially, for those students whose school is not located in the city. Thus, their English abilities in speaking, reading, and listening are poor. An intelligent tutoring system is used to help the students improve their English capabilities. This paper aims to provide a convenient tutoring environment, where teachers and students do not need to prepare a lot of teaching aids. They can teach and learn English whenever in the environment. Also, it proposes a method to verify the intelligent tutoring system using Petri nets. We have built the intelligent tutoring system based on Augmented Reality (AR), Text-to-Speech (TTS), and Speech Recognition (SR). This intelligent tutoring system is divided into two parts: one for teachers and the other for students. The experimental results have indicated that using Petri nets can help users verify the intelligent tutoring system for better learning performance and operate it correctly.
- intelligent tutoring system,
- augmented reality,
- text-to-speech,
- speech recognition,
- petri net
Citation: Yu-Ying Wang, Ah-Fur Lai, Rong-Kuan Shen, Cheng-Ying Yang, Victor R.L. Shen, Ya-Hsuan Chu. Modeling and verification of an intelligent tutoring system based on Petri net theory[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4947-4975. doi: 10.3934/mbe.2019250

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Abstract

According to the educational regulations in Taiwan, students are required to learn English when they are at the first grade of elementary school. However, not all the students have an appropriate environment to practice English, especially, for those students whose school is not located in the city. Thus, their English abilities in speaking, reading, and listening are poor. An intelligent tutoring system is used to help the students improve their English capabilities. This paper aims to provide a convenient tutoring environment, where teachers and students do not need to prepare a lot of teaching aids. They can teach and learn English whenever in the environment. Also, it proposes a method to verify the intelligent tutoring system using Petri nets. We have built the intelligent tutoring system based on Augmented Reality (AR), Text-to-Speech (TTS), and Speech Recognition (SR). This intelligent tutoring system is divided into two parts: one for teachers and the other for students. The experimental results have indicated that using Petri nets can help users verify the intelligent tutoring system for better learning performance and operate it correctly.

References

[1]	H. K. Wu, S. W. Y. Lee, H. Y. Chang, et al., Current status, opportunities and challenges of augmented reality in education, Comput. Educ., 62 (2013), 41–49.
[2]	Y. B. Kim and S. Y. Rhee, Augmented reality on a deformable surface by considering self-occlusion and realistic illu, J. Internet Technol., 12 (2011), 733–740.
[3]	B. Koo and T. Shon, A structural health monitoring framework using 3D visualization and augmented reality in wireless sensor networks, J. Internet Technol., 11 (2010), 801–807.
[4]	G. Schall, E. Mendez, E. Kruijff, et al., Handheld augmented reality for underground infrastructure visualization, Pers. Ubiquit. Comput., 13 (2009), 281–291.
[5]	K. J. L. Nevelsteen, Virtual world, defined from a technological perspective and applied to video games, mixed reality, and the Metaverse, Comput. Animat. Virt. W., 29 (2017).
[6]	J. Luo, Q. Zhang, X. Chen, et al., Modeling and race detection of ladder diagrams via ordinary Petri nets, IEEE T. Syst., Man. Cy. S., 48 (2018), 1166–1176.
[7]	B. J. Boom, S. Orts-Escolano, X. X. Ning, et al., Interactive light source position estimation for augmented reality with an RGB-D camera, Comput. Animat. Virt. W., 28 (2017).
[8]	P. C. Xiong, Y. S. Fan and M. C. Zhou, A Petri net approach to analysis and composition of Web services, IEEE T. Syst. Man. Cy. A., 40 (2010), 376–387.
[9]	Y. Ru and C. N. Hadjicostis, Bounds on the number of markings consistent with label observations in Petri nets, IEEE T. Autom. Sci. Eng., 6 (2009), 334–344.
[10]	J. Rosell, Assembly and task planning using Petri nets: A survey, J. Eng. Manuf., 218 (2004), 987–994.
[11]	L. Li, Y. Ru and C. N. Hadjicostis, Least-cost firing sequence estimation in labeled Petri nets, Process of the 45^th IEEE International Conference on Decision and Control, (2006), 416–421.
[12]	D. M. Xiang, G. J. Liu, C. G. Yan, et al., Detecting data-flow errors based on Petri nets with data operations, IEEE/CAA JAS, 5 (2018), 251–260.
[13]	T. Murata, Petri nets: properties, analysis, and applications, IEEE, 77 (1989), 541–580.
[14]	N. Wu and M. C. Zhou, System modeling and control with resource-oriented Petri nets, Int. J. Prod. Res., 49 (2011), 6585–6586.
[15]	WoPeD, Mar. 2017. Available from: http://woped.dhbw-karlsruhe.de/woped/.
[16]	F. Suro and Y. Ono, Japanese EFL learners' uses of text-to-speech technology and their learning behaviors: A pilot study, Process of the 2016 5^th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), (2016), 296–301.
[17]	Y. Lee, S. Lee and S. H. Lee, Multifinger interaction between remote users in avatar-mediated telepresence, Comput. Animat. Virt. W., 28 (2017).
[18]	Speaker independent connected speech recognition-fifth generation computer corporation, May 2017. Available from: http://www.fifthgen.com/speaker-independent-connected-s-r.htm.
[19]	F. J. Yang, N. Q. Wu, Y. Qiao, et al., Polynomial approach to optimal one-wafer cyclic scheduling of treelike hybrid multi-cluster tools via Petri nets, IEEE/CAA JAS, 5 (2018), 270–280.
[20]	P. Milgram and A. F. Kishino, Taxonomy of mixed reality visual displays, IEICE T. Inform. Syst., (1994), 1321–1329.
[21]	K. C. Li, C. W. Tsai, C. T. Chen, et al., The design of immersive English learning environment using augmented reality, Process of 8^th IEEE International Conference on UMEDIA, (2015), 174–179.
[22]	C. S. C. Dalim, A. Dey and T. Piumsomboon, TeachAR: An interactive augmented reality tool for teaching basic English to non-native children, Process of IEEE International Symposium on Mixed and Augmented Reality Adjunct, (2016), 67–72.
[23]	F. Sorrentino, L. D. Spano and R. Scateni, Speaky notes learn languages with augmented reality, Process of IEEE International Conference Interactive Mobile Communication Technologies and Learning (IMCL), (2015), 56–61.

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