1. bookVolume 6 (2016): Issue 2 (April 2016)
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 10 Mar 2016
Page range: 81 - 101
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

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.

Keywords

[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.Search in Google Scholar

[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.Search in Google Scholar

[3] T. Kidd, Online Education and Adult Learning: New York: Hershey, 2010.Search in Google Scholar

[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.Search in Google Scholar

[5] Ambient Insight, Learning Technology Research Taxonomy Research Methodology, Buyer Segmentation, Product Definitionsand Licensing Model, Ambient Insight Research, 2012.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

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

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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. 1-4,2005Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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. Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo