Title: Enhancing the Adaptive E-learning Environment by using the Markov Decision Process (MDP)
Year of Publication: 2018
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 05, Number 9, September 2018
Authors: Norah Alqahtani , Mahmod Kamel, Mostafa Saleh


Norah Alqahtani , Mahmod Kamel, Mostafa Saleh, "Enhancing the Adaptive E-learning Environment by using the Markov Decision Process (MDP)", In International Journal of Computer Systems (IJCS), pp: 43-46, Volume 5, Issue 9, September 2018. BibTeX

	author = {Norah Alqahtani , Mahmod Kamel, Mostafa Saleh},
	title = {Enhancing the Adaptive E-learning Environment by using the Markov Decision Process (MDP)},
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2018},
	volume = {5},
	number = {9},
	pages = {43-46},
	month = {September}


Adaptive learning assists by increasing the number of learners, as it overcome barriers to learning such as distance and time factors. Recently, there has been much research into adaptive e-learning, which has helped to improve the learning process. This paper discusses some models that have been used to improve adaptive e-learning systems and suggests that the Markov Decision Process (MDP) should be used to improve adaptivity in the learning process. The results indicate that the MDP can help in the development of adaptive e-learning.


[1] Adaptive content creation for personalized e-learning using Web services.
[2] E-learning personalization based on dynamic learners' preferences.
[3] K.Kruse, “The Benefits and Drawbacks of e-Learning”,15.01.2010, 2004.
[4] C.Chen, “Personalized e-learning system with self-regulated learning assisted mechanisms for promoting learning performance,” Expert Systems with Applications, 36, 8816–8829, 2009.
[5] P.Brusilovsky, “Adaptive and intelligent technologies for Webbased education.” In C. Rollinger and C. Peylo (eds.), Special Issue on Intelligent Systems and Teleteaching, KünstlicheIntelligenz, 4, 19–25, 1999.
[6] S.Stoyanov and P.Kirschner, "Expert concept mapping method for defining the characteristics of adaptive e-learning: ALFANET project case,” Educational Technology, Research & Development, 52 (2) 41–56, 2004.
[7] Illustrating an ideal adaptive e-learning: A conceptual framework.
[8] White Paper: Adaptive Learning Systems.
[9] User modeling for adaptive e-learning systems.
[10] Creating adaptive environment for e-learning courses.
[11] J.Kuljis and F.Liu, A comparison of learning style theories on the suitability for e-learning. In Hamza, M.H. (ed.) Proceedings of the IASTED Conferenceon Web Technologies.
[12] N. Idris et al. Applications and Services, ACTA Press, Calgary, pp. 191–197, 2005.
[13] Adaptive course sequencing for personalization of learning path using neural network.
[14] A fully personalization strategy of e-learning scenarios.
[15] Baylari and Montazer. Design a personalized e-learning system based on item response theory and artificial neural network approach.
[16] Yasir and Sami. An approach to adaptive e-learning hypermedia system based on learning styles (AEHS-LS): Implementation and evaluation.
[17] A learning design recommendation system based on Markov Decision Processes.
[18] C.Boutilier, M.Goldszmidt, and B.Sabata, “Sequential auction for the allocation of resources with complementarities.” In Proceedings of IJCAI 99, Stockholm, volume 1, pp. 527–534, 1999.
[19] M. Moghadasi, A. Haghighat, and S. Ghidary, "Evaluating Markov Decision Process as a model for decision making under uncertainty environment", Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, pp. 9–22, 2007.
[20] C. Zhang, M. Dong, Y. Ji, and Y. Tanaka, "Markov-Decision-Process-assisted consumer scheduling in a networked smart grid," 2016.
[21] L. Kallenberg, Markov Decision Processes. Leiden, 2016.
[22] T. Hawk and A. Shah, "Using learning style instruments to enhance student learning", Decision Sciences Journal of Innovative Education, 5(1); 1–19, 2007.
[23] S. Skiena, Programming challenges. Dordrecht: Springer, 2004.


e-learning, adaptive, adaptive learning, learning style, Markov Decision Process, dynamic programming.