An Adaptive Method Based on High-Level Petri Nets for E-Learning

Fatemeh Omrani, Ali Harounabadi, Vahid Rafe
DOI: 10.4236/jsea.2011.410065                

Abstract

Adaptive learning is a new approach for e-learning systems. In comparison to traditional e-learning systems, which present same things for all learners, these systems automatically adapt with learner characteristics. In this paper, we are going to propose a new method for Adaptive learning, and consider adaptation from three viewpoints: 1) learner learning style, 2) learner’s knowledge level, 3) learner’s score. Due to similarity between learning objects graph and petri net, and In order to provide adaptive learning, we use an approach based on a high level petri net (HLPN).Also we propose a method to evaluate performance in this system. We compare our system with a non adaptive system, through our performance evaluating method. The results show response time for our system is less than non adaptive system and learners finish course in a relatively shorter period of time. Since our proposed system considers individual features of learner, we can be sure that learner would not be confused in learning materials.

Keywords

Adaptive Learning, Learning Style, High Level Petri Nets, Response Time

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. Dahbi, N. Elkamoun and A. Berraissoul, “Adaptation and Optimisation of Pedagogical Paths by Ants’s Algorithm,” ICTTA’06, 2nd Information and Communication Technologies, Damascus, October 2006, pp. 546-551.
[2]	S. Gao and R. Dew, “Enhancing Web-Based Adaptive Learning with Colored Timed Petri Net,” Lecture Notes in Computer Science, Vol. 4798, 2007, pp. 177-185. doi:10.1007/978-3-540-76719-0_20
[3]	S. Gao, Z. Zhang and I. Hawryszkiewycz, “Supporting Adaptive Learning in Hypertext Environments: A High Level Timed Petri Net-Based Approach,” International Journal of Intelligent Systems Technologies and Applications, Vol. 4, No. 3-4, 2008, pp. 341-354. doi:10.1504/IJISTA.2008.017277
[4]	Y. Semet, E. Lutton and P. Collet, “Ant Colony Optimisation for E-Learning: Observing the Emergence of Pedagogic Suggestions,” Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, 24-26 April 2003, pp. 46-52.
[5]	L. de Marcos, J. J. Martínez and J. A. Gutierrez, “Swarm Intelligence in E-Learning: A Learning Object Sequencing Agent Based on Competencies,” Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, Atlanta, 12-16 July 2008, pp. 17-24.
[6]	F. Zhu and J. Cao, “Learning Activity Sequencing in Personalized Education System,” Wuhan University Journal of Natural Sciences, Vol. 13, No. 4, 2008, pp. 461-465. doi:10.1007/s11859-008-0416-6
[7]	C. M. Chen, H. M. Lee and Y. H. Chen, “Personalized E-Learning System Using Item Response Theory,” Computers & Education, Vol. 44, No. 3, 2005, pp. 237-255. doi:10.1016/j.compedu.2004.01.006
[8]	N. Manouselis and D. Sampson, “Dynamic Knowledge Route Selection for Personalised Learning Environments Using Multiple Criteria,” 20th IASTED International Conference in Applied Informatics, Innsbruck, 18-21 February 2002, pp. 448-453.
[9]	J.-N. Chen, Y.-M. Huang and W. C.-C. Chu, “Applying Dynamic Fuzzy Petri Net to Web Learning System,” Interactive Learning Environments, Vol. 13, No. 3, 2005, pp. 159-178. doi:10.1080/10494820500382810
[10]	Y. C. Chang, Y. C. Huang and C. P. Chu, “B2 Model: A Browsing Behavior Model Based on High-Level Petri Nets to Generate Behavioral Patterns for E-Learning,” Expert Systems with Applications, Vol. 36, No. 10, 2009, pp. 12423-12440. doi:10.1016/j.eswa.2009.04.044
[11]	J. M. Su, S. S. Tseng, C. Y. Chen, J. F. Weng and W. N. Tsai, “Constructing SCORM Compliant Course Based on High-Level Petri Nets,” Computer Standards & Interfaces, Vol. 28, No. 3, 2006, pp. 336-355. doi:10.1016/j.csi.2005.04.001
[12]	X. Q. Liu, M. Wu and J. X. Chen, “Knowledge Aggregation and Navigation High-Level Petri Nets-Based in E- Learning,” International Conference on Machine Learning and Cybernetics, Vol. 1, 2002, pp. 420-425.
[13]	R. M. Felder and L. K. Silverman, “Learning and Teaching Styles in Engineering Education,” Engineering Education, Vol. 78, No. 7, 1988, pp. 674-681.
[14]	P. García, A. Amandi, S. Schiaffino and M. Campo, “Evaluating Bayesian Networks’ Precision for Detecting Students’ Learning Styles,” Computers & Education, Vol. 49, No. 3, 2007, pp. 794-808. doi:10.1016/j.compedu.2005.11.017
[15]	T. I. Wang, K. T. Wang and Y. M. Huang, “Using a Style-Based Ant Colony System for Adaptive Learning,” Expert Systems with Applications, Vol. 34, No. 4, 2008, pp. 2449-2464. doi:10.1016/j.eswa.2007.04.014
[16]	W. A. Drago and R. J. Wagner, “Vark Preferred Learning Styles and Online Education,” Management Research News, Vol. 27, No. 7, 2004, pp. 1-13. doi:10.1108/01409170410784211
[17]	T. Murata, “Petri Nets: Properties, Analysis and Applications,” Proceedings of the IEEE, Vol. 77, No. 4, 1989, pp. 541-580.
[18]	P. Abdulla and A. Nylén, “Timed Petri Nets and BQOs,” Applications and Theory of Petri Nets, Vol. 2075, 2001, pp. 53-70. doi:10.1007/3-540-45740-2_5
[19]	S. Christensen, L. Kristensen and T. Mailund, “Condensed State Spaces for Timed Petri Nets,” Proceedings of the 22nd International Conference on Application and Theory of Petri Nets, Newcastle, 25-29 June 2001, pp. 101-120.
[20]	R. S. Shaw, “A Study of Learning Performance of E-Learning Materials Design with Knowledge Maps,” Computers & Education, Vol. 54, No. 1, 2010, pp. 253-264. doi:10.1016/j.compedu.2009.08.007
[21]	R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye, “Probability and Statistics for Engineers and Scientists,” 7th Edition, Prentice Hall, Upper Saddle River, 2002.
[22]	L. Wells, “Performance Analysis Using Coloured Petri Nets,” 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, Fort Worth, 11-16 October 2002, pp. 217-221.
[23]	K. Jensen, L. M. Kristensen and L. Wells, “Coloured Petri Nets and CPN Tools for Modelling and Validation of Concurrent Systems,” International Journal on Software Tools for Technology Transfer, Vol. 9, No. 3-4, 2007, pp. 213-254. doi:10.1007/s10009-007-0038-x
[24]	A. Ratzer, L. Wells, H. Lassen, M. Laursen, J. Qvortrup, M. Stissing, M. Westergaard, S. Christensen and K. Jensen, “CPN Tools for Editing, Simulating, and Analysing Coloured Petri Nets,” Proceedings of the 24th International Conference on Applications and Theory of Petri Nets, Eindhoven, 23-27 June 2003, pp. 450-462.
[25]	R. S. Shaw, “A Study of Learning Performance of E-Learning Materials Design with Knowledge Maps,” Computers & Education, Vol. 54, No. 1, 2010, pp. 253-264. doi:10.1016/j.compedu.2009.08.007