Adaptive E-Learning System Using Fuzzy Logic and Concept Map
This paper proposes an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the chosen subject area. A Fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain, and produce a ranked concept list of learning materials to address weaknesses in the learner’s understanding. This system obtains information on the learner's understanding of concepts by an initial pre-test before the system is used for learning and a post-test after using the learning system. A Fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance learner's performance and understanding. In addition, this research aims to increase participants' overall understanding of their learning level by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474381Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 333
 Akscyn, R. M., D. L. McCracken, and E. A. Yoder, KMS: a distributed hypermedia system for managing knowledge in organizations. Communications of the ACM, 1988. 31(7): p. 820-835.
 Moore, J. L., C. Dickson-Deane, and K. Galyen, e-Learning, online learning, and distance learning environments: Are they the same? The Internet and Higher Education, 2011. 14(2): p. 129-135.
 Liaw, S.-S., Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 2008. 51(2): p. 864-873.
 Conlan, O., D. Dagger, and V. Wade. Towards a standards-based approach to e-Learning personalization using reusable learning objects. in Proc. of World Conference on E-Learning, E-Learn. 2002.
 Pechenizkiy, M., et al. Mining the student assessment data: Lessons drawn from a small scale case study. in Educational Data Mining 2008. 2008.
 Khemaja, M. and A. Taamallah, Towards Situation Driven Mobile Tutoring System for Learning Languages and Communication Skills: Application to Users with Specific Needs. Educational Technology & Society, 2016. 19(1): p. 113-128.
 Klašnja-Milićević, A., et al., Personalization and Adaptation in E-Learning Systems, in E-Learning Systems. 2017, Springer. p. 21-25.
 Vanides, J., et al., Concept maps. Science Scope, 2005. 28(8): p. 27-31.
 Hsieh, T.-C., M.-C. Lee, and C.-Y. Su, Designing and implementing a personalized remedial learning system for enhancing the programming learning. Educational Technology & Society, 2013. 16(4): p. 32-46.
 Stansfield, J.L., Wumpus Advisor I. A First Implementation of a Program That Tutors Logical and Probabilistic Reasoning Skills. AI Memo 381. 1976.
 Rich, E., User modeling via stereotypes. Cognitive science, 1979. 3(4): p. 329-354.
 Liebowitz, J., Expert systems: A short introduction. Engineering Fracture Mechanics, 1995. 50(5): p. 601-607.
 Ben‐Gal, I., Bayesian networks. Encyclopedia of statistics in quality and reliability, 2007.
 Dogan, B. and E. Dikbıyık, OPCOMITS: Developing an adaptive and intelligent web based educational system based on concept map model. Computer Applications in Engineering Education, 2016. 24(5): p. 676-691.
 Violante, M. G. and E. Vezzetti, Design of web-based interactive 3D concept maps: A preliminary study for an engineering drawing course. Computer Applications in Engineering Education, 2015. 23(3): p. 403-411.
 Awati, M. A. S. and A. Dixit, Automated Evaluation Framework for Student Learning using Concept Maps. 2017.
 Zadeh, L. A., Fuzzy sets. Information and control, 1965. 8(3): p. 338-353.
 Zadeh, L. A., Fuzzy logic= computing with words. IEEE transactions on fuzzy systems, 1996. 4(2): p. 103-111.
 Hájek, P., What is mathematical fuzzy logic. Fuzzy sets and systems, 2006. 157(5): p. 597-603.
 Zadeh, L. A., Making computers think like people (fuzzy set theory). IEEE spectrum, 1984. 21(8): p. 26-32.
 Yen, J., L. Wang, and C.W. Gillespie, Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Transactions on fuzzy Systems, 1998. 6(4): p. 530-537.
 Zimmermann, H.-J., Fuzzy set theory—and its applications. 2011: Springer Science & Business Media.
 Chrysafiadi, K. and M. Virvou, Evaluating the integration of fuzzy logic into the student model of a web-based learning environment. Expert Systems with Applications, 2012. 39(18): p. 13127-13134.
 Chrysafiadi, K. and M. Virvou, A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system. SpringerPlus, 2013. 2(1): p. 81.
 Chrysafiadi, K. and M. Virvou, Fuzzy logic for adaptive instruction in an e-learning environment for computer programming. IEEE transactions on Fuzzy Systems, 2015. 23(1): p. 164-177.
 Jeremić, Z., J. Jovanović, and D. Gašević, Student modeling and assessment in intelligent tutoring of software patterns. Expert Systems with Applications, 2012. 39(1): p. 210-222.
 Zafar, A. and I. Albidewi, Evaluation study of eLGuide: A framework for adaptive e-learning. Computer Applications in Engineering Education, 2015. 23(4): p. 542-555.
 Kirkpatrick, D. L., Techniques for evaluating training programs. Training and development journal, 1979.
 Jeremić, Z., J. Jovanović, and D. Gašević, Evaluating an intelligent tutoring system for design patterns: The DEPTHS experience. Educational Technology & Society, 2009. 12(2): p. 111-130.
 Negnevitsky, Michael, Artificial intelligence: a guide to intelligent systems, 2005.