Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Khalid Almohammadi 1 , Hani Hagras 1 , Daniyal Alghazzawi 2  and Ghadah Aldabbagh 1
  • 1 The Computational Intelligence Centre, School of Computer Science and Electronic Engineering University of Essex, Colchester, UK
  • 2 Information Systems Department, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, Saudi Arabia


Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved.

This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] LA. James, Evaluation of an Adaptive Learning Technology as a Predictor of Student Performance in Undergraduate Biology, (Master’s thesis), Appalachian State University, North Carolina,USA, May 2012.

  • [2] B. Bloom, The 2 sigma problem: The search for methods of group instruction as effective as one-toone tutoring, Educ. Res., vol. 13, pp. 4-16, 1984.

  • [3] T. Kidd, Online Education and Adult Learning: New York: Hershey, 2010.

  • [4] M. Vandewaetere, P. Desmet, and G. Clarebout, The contribution of learner characteristics in the development of computer-based adaptive learning environments, Computers in Human Behavior, vol.27, No.1, pp.118-130, 2011.

  • [5] Ambient Insight, Learning Technology Research Taxonomy Research Methodology, Buyer Segmentation, Product Definitionsand Licensing Model, Ambient Insight Research, 2012.

  • [6] B. Ciloglugil, and M. Inceoglu, User Modeling for Adaptive E-Learning Systems, Computational Science and Its Applications-ICCSA 2012, vol.7335, pp.5561, 2012.

  • [7] F. Essalmi, L. J. B. Ayed, M. Jemni, Kinshuk, and S. Graf, A fully personalization strategy of Elearning scenarios, Computers in Human Behavior, Elsevier, vol.26, No.4, pp.581-591, 2010.

  • [8] V. J. Shute, and D. Zapata-Rivera, Adaptive educational systems, In P. Durlach (Ed.), Adaptive technologies for training and education (pp. 7-27). New York, NY: Cambridge University Press, 2012.

  • [9] White paper based upon the Speak Up 2011 national findings, Leveraging Intelligent Adaptive Learning to Personalize Education, Intelligent Adaptive Learning : Speak Up Reports, 2012.

  • [10] C. Martins, L. Faria, and E. Carrapatoso, An Adaptive Educational System For Higher Education, Proceedings of the 14th EUNIS 08 International Conference of European University Information Systems, Denmark, 24 - 27 of June, 2008.

  • [11] A. Ahmad,O. Basir, and K. Hassanein,Adaptive user interfaces for intelligent e-Learning: issues and trends, In Proceedings of the Fourth International Conference on Electronic Business (ICEB2004), Xiyuan Hotel, Beijing, China, pp. 925-934, December 5-9, 2004.

  • [12] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall PTR, Prentice Hall Inc, 2001.

  • [13] E. Frias-Martinez, G. Magoulas, S. Chen, and R Macredie, Recent soft computing approaches to user modeling in adaptive hypermedia, In Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 31-55, Springer Berlin/Heidelberg, 2004.

  • [14] A. Gertner, and K.VanLehn, Andes: A coached problem solving environment for physics, In Intelligent Tutoring Systems, vol.1839, pp. 133-142, Springer Berlin/Heidelberg, 2000.

  • [15] N. Idris, N. Yusof, and P. Saad, Adaptive course sequencing for personalization of learning path using neural network, International Journal of Advanced Soft Computing Applications, vol. 1, pp. 49-61, 2009.

  • [16] H. Seridi-Bouchelaghem, T. Sari, and M. Sellami, A Neural Network for Generating Adaptive Lessons, Journal of Computer Science 1, no. 2, pp.232-243, 2005.

  • [17] R. Sripan and B. Suksawat, Propose of Fuzzy Logic-Based Students’ Learning Assessment, Proceedings in the International Conference on Control, Automation and Systems, pp. 414-417, Gyeonggi-do, Korea, October 2010.

  • [18] J. Ma and D. N. Zhou, Fuzzy set approach to the assessment of student centered learning, IEEE Trans. Educ., vol. 33, pp. 237-241, May 2000.

  • [19] S. Venkatesan, and S. Fragomeni, Evaluating learning outcomes in PBL using fuzzy logic techniques, 19th Annual Conference of the Australasian Association for Engineering Education: To Industry and Beyond; Proceedings of the Institution of Engineers, Australia, 2008.

  • [20] D. Xu, H. Wang and K. Su, Intelligent student profiling with fuzzy models, in Proceedings of the 35th Hawaii International Conference on System Science (HICSS 2002), January, Hawaii, U.S.A, 2002.

  • [21] H.J. Cha, Y.S. Kim, S.H. Park, T.B. Yoon, Y.M. Jung, and J.-H. Lee, Learning Style Diagnosis Based on User Interface Behavior for the Customization of Learning Interfaces in an Intelligent Tutoring System, Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science, Berlin, Heidelberg, Springer, Vol. 4053, pp. 513-524, 2006.

  • [22] F. Moreno, A. Carreras, M. Moreno and E. R. Royo, Using Bayesian Networks in the Global Adaptive E-learning Process, EUNIS 2005, Manchester, pp. 1-4,2005

  • [23] S. Gutierrez-Santos, J. Mayor-Berzal,C. Fernandez-Panadero, and CR. Kloos, Authoring of Probabilistic Sequencing in Adaptive Hypermedia with Bayesian Networks, Journal of Universal Computer Science 16, no. 19, pp.2801-2820, 2010.

  • [24] R. Stathacopoulou, M. Grigoriadou, M. Samarakou, and D. Mitropoulos, Monitoring students’ actions and using teachers’ expertise in implementing and evaluating the neural networkbased fuzzy diagnostic model, Expert Systems with Applications, Elsevier, 32, pp. 955-975, 2007.

  • [25] A. Jameson, Numerical uncertainty management in user and student modeling: An overview of systems and issues, Use Modeling and User-adapted Interaction, vol. 5(3-4), pp. 103-251, 1996.

  • [26] A. Kavi, R. Pedraza-Jimnez, H. Molina-Bulla, F.J. Valverde-Albacete, J. Cid-Sueiro, and A. Navia- Vzquez, Student Modelling Based on Fuzzy Inference Mechanisms, Proceedings of the IEEE Region 8 EUROCON 2003, Computer as a Tool, Ljubljana, Slovenia, September 2003.

  • [27] A. Kavi, Fuzzy user modeling for adaptation in educational hypermedia, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 4, pp. 439-449, Nov. 2004.

  • [28] F. Liu, and J. Mendel, An interval approach to Fuzzistics for intervaltype-2 fuzzy sets, Proceedings of the 2007 IEEE InternationalConference on Fuzzy Systems, London, UK, pp. 1030-1035.

  • [29] K. Almohammadi, B. Yao, and H. Hagras, An interval type-2 fuzzy logic based system with user engagement feedback for customized knowledge delivery within intelligent E-learning platforms, Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, 2014, pp. 808-817.

  • [30] K. Almohammadi and H. Hagras, An Interval Type-2 Fuzzy Logic Based System for Customised Knowledge Delivery within Pervasive E-Learning Platforms, Proceeings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, pp. 2872-2879.

  • [31] K. Almohammadi, H. Hagras, B. Yao, A. Alzahrani, D.Alghazzawi, and G. Aldabbagh, A Type-2 Fuzzy Logic Recommendation System for Adaptive Teaching, Journal of Soft Computing, August 2015.

  • [32] L. X. Wang, The MW method completed: A flexible system approachto data mining, IEEE Transactions on Fuzzy Systems, vol. 11, no. 6, pp. 768-782, December 2003.

  • [33] H. Hagras, F. Doctor, A. Lopez and V.Callaghan, An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments, IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 41-55, February 2007.

  • [34] K. Almohammadi and H. Hagras, An adaptive fuzzy logic based system for improved knowledge delivery within intelligent E-Learning platforms, Proccedings of the the 2013 IEEE International Conference on Fuzzy Systems, 2013, pp. 1-8.


Journal + Issues