Abstract
E-learning systems have the ability to facilitate the interaction between learners and teachers without being limited by temporal and/or spatial constraints. However, the high number of students at universities, the huge number of available learning in the web, the differences between learners in term of characteristics and needs make the traditional e-learning systems more limited. For this purpose, adaptive learning has been recently explored in order to cope with these limitations and to meet the individual needs of learner. In this context, many artificial intelligence methods and approaches have been integrated in such computer-based systems in order to create effective learner models, structured domain models, adaptive learning paths, personalized learning format, etc. Such methods are highly recommended for designing adaptive e-learning and m-learning systems with good quality. In this paper, we focus only on one of these methods, called fuzzy logic, which is widely used in educational area. We present the integration of fuzzy logic as a valuable approach that has the ability to deal with the high level of uncertainties and imprecision related to learners’ characteristics and learning contexts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Almohammadi, K., Hagras, H., Yao, B., Alzahrani, A., Alghazzawi, D., Aldabbagh, G.: A type-2 fuzzy logic recommendation system for adaptive teaching. Soft Comput. 21, 965–979 (2017). https://doi.org/10.1007/s00500-015-1826-y
Mergler, A.G., Spooner-Lane, R.S.: What pre-service teachers need to know to be effective at values-based education. Aust. J. Teach. Educ. 37, 66–81 (2012)
Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: Providing personalized assistance to e-learning students. Comput. Educ. 51, 1744–1754 (2008). https://doi.org/10.1016/j.compedu.2008.05.008
Almohammadi, K., Hagras, H.: An adaptive fuzzy logic based system for improved knowledge delivery within intelligent e-learning platforms. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). pp. 1–8 (2013)
Zhu, Z.-T., Yu, M.-H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3, 4 (2016). https://doi.org/10.1186/s40561-016-0026-2
Hoel, T., Mason, J.: Standards for smart education – towards a development framework. Smart Learn. Environ. 5, 3 (2018). https://doi.org/10.1186/s40561-018-0052-3
Uskov, V.L., Bakken, J.P., Heinemann, C., Rachakonda, R., Guduru, V.S., Thomas, A.B., Bodduluri, D.P.: Building smart learning analytics system for smart university. In: Uskov, V.L., Howlett, R.J., and Jain, L.C. (eds.) Smart Education and e-Learning 2017. pp. 191–204. Springer, Berlin (2018)
Zhu, Z.T., Bin, H.: Smart education: a new paradigm in educational technology. Telecommun. Educ. 12, 3–15 (2012)
Zhao, C., Wan, L.: A shortest learning path selection algorithm in e-learning. In: Sixth IEEE International Conference on Advanced Learning Technologies (ICALT’06). pp. 94–95 (2006)
Ennouamani, S., Mahani, Z.: An overview of adaptive e-learning systems. In: 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS). pp. 342–347 (2017)
Tan, H., Guo, J., Li, Y.: E-learning recommendation system. In: 2008 International Conference on Computer Science and Software Engineering. pp. 430–433 (2008)
Cavus, N., Bicen, H., Akcil, U.: The Opinions of Information Technology Students on Using Mobile Learning (2008)
Naismith, L., Lonsdale, P., Vavoula, G.N., Sharples, M.: Mobile Technologies and Learning (2004)
Rahamat, R.B., Shah, P.M., Din, R.B., Aziz, J.B.A.: Students’ readiness and perceptions towards using mobile technologies for learning the english language literature component. Engl. Teach. 8, 16 (2017)
Traxler, J., Kukulska-Hulme, A.: Mobile Learning: A Handbook for Educators and Trainers. Routledge, Abingdon (2007)
Chan, T.-W., Roschelle, J., Hsi, S., Kinshuk, Sharples, M., Brown, T., Patton, C., Cherniavsky, J., Pea, R., Norris, C., Soloway, E., Balacheff, N., Scardamalia, M., Dillenbourg, P., Looi, C.-K., Milrad, M., Hoppe, U.: One-to-one technology-enhanced learning: an opportunity for global research collaboration. Res. Pract. Technol. Enhanc. Learn. 01, 3–29 (2006). https://doi.org/10.1142/s1793206806000032
Norris, C.A., Soloway, E.: Learning and schooling in the age of mobilism. Educ. Technol. 51, 3–10 (2011)
Wu, W.-H., Jim Wu, Y.-C., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., Huang, S.-H.: Review of trends from mobile learning studies: a meta-analysis. Comput. Educ. 59, 817–827 (2012). https://doi.org/10.1016/j.compedu.2012.03.016
Kukulska-Hulme, A.: How should the higher education workforce adapt to advancements in technology for teaching and learning? Internet High. Educ. 15, 247–254 (2012). https://doi.org/10.1016/j.iheduc.2011.12.002
Kidd, T.T.: Online education and adult learning: new frontiers for teaching practices. Information Science Reference (2010)
BLOOM, B.S.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Res. 13, 4–16 (1984). https://doi.org/10.3102/0013189x013006004
Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40, 4715–4729 (2013). https://doi.org/10.1016/j.eswa.2013.02.007
Pandey, H., Singh, V.K.: A fuzzy logic based recommender system for e- learning system with multi-agent framework. Int. J. Comp. Appl. 122(17), 0975–8887 (2015)
V. J. Shute, D.Z.-R.: Adaptive Educational Systems (2012)
Boticario, J., Santos, O., Van Rosmalen, P.: Issues in developing standard-based adaptive learning management systems. Presented at the EADTU 2005 working conference: Towards Lisbon 2010: Collaboration for innovative content in lifelong open and flexible learning. (2005)
Kass, R.: Building a user model implicitly from a cooperative advisory dialog. User Model. User-Adapt. Interact. 1, 203–258 (1991). https://doi.org/10.1007/bf00141081
Moore, M.G.: Editorial: three types of interaction. Am. J. Distance Educ. 3, 1–7 (1989). https://doi.org/10.1080/08923648909526659
Alshammari, M., Anane, R., Hendley, R.J.: Adaptivity in e-learning systems. In: 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems, pp. 79–86 (2014)
Ennouamani, S., Mahani, Z.: Designing a practical learner model for adaptive and context-aware mobile learning systems. IJCSNS Int. J. Comput. Sci. Netw. Secur. 18, 84–93 (2018)
Millán, E., Loboda, T., Pérez-de-la-Cruz, J.L.: Bayesian networks for student model engineering. Comput. Educ. 55, 1663–1683 (2010). https://doi.org/10.1016/j.compedu.2010.07.010
Nguyen, L., Do, P.: Combination of Bayesian network and overlay model in user modeling. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational Science – ICCS 2009. pp. 5–14. Springer, Heidelberg (2009)
Zadeh, L.A.: Information and control. Fuzzy Sets. 8, 338–353 (1965)
Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160, 76–94 (2009). https://doi.org/10.1016/j.fss.2008.03.017
Al-Shamri, M.Y.H., Bharadwaj, K.K.: Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst. Appl. 35, 1386–1399 (2008). https://doi.org/10.1016/j.eswa.2007.08.016
Drigas, A.S., Argyri, K., Vrettaros, J.: Decade Review (1999–2009): Artificial intelligence techniques in student modeling. In: Lytras, M.D., Ordonez de Pablos, P., Damiani, E., Avison, D., Naeve, A., and Horner, D.G. (eds.) Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All, pp. 552–564. Springer, Heidelberg (2009)
Shakouri G., H., Tavassoli N., Y.: Implementation of a hybrid fuzzy system as a decision support process: a FAHP–FMCDM–FIS composition. Expert Syst. Appl. 39, 3682–3691 (2012). https://doi.org/10.1016/j.eswa.2011.09.063
Amindoust, A., Ahmed, S., Saghafinia, A., Bahreininejad, A.: Sustainable supplier selection: a ranking model based on fuzzy inference system. Appl. Soft Comput. 12, 1668–1677 (2012). https://doi.org/10.1016/j.asoc.2012.01.023
Vandewaetere, M., Desmet, P., Clarebout, G.: The contribution of learner characteristics in the development of computer-based adaptive learning environments. Comput. Hum. Behav. 27, 118–130 (2011). https://doi.org/10.1016/j.chb.2010.07.038
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8, 199–249 (1975). https://doi.org/10.1016/0020-0255(75)90036-5
Aessilan, S., Mamdani, E.: An experiment in linguistic synthesis of fuzzy logic controllers. Int. J. Man-Mach. Stud. 7, 1–13 (1974)
Khan, F.A., Shahzad, F., Altaf, M.: Fuzzy based approach for adaptivity evaluation of web based open source learning management systems. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1036-8
Takagi, T., Sugeno, M.: Derivation of fuzzy control rules from human operator’s control actions. IFAC Proc. 16, 55–60 (1983). https://doi.org/10.1016/s1474-6670(17)62005-6
Jang, J.-R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993). https://doi.org/10.1109/21.256541
Chen, C.-M.: A fuzzy-based decision-support model for rebuy procurement. Int. J. Prod. Econ. 122, 714–724 (2009). https://doi.org/10.1016/j.ijpe.2009.06.037
Mohamed, F., Abdeslam, J., Lahcen, E.B.: Personalization of learning activities within a virtual environment for training based on fuzzy logic theory. In: International Association for the Development of the Information Society (2017)
Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to fuzzy logic using MATLAB. Springer, Berlin (2007)
Wallace, M., Ioannou, S., Karpouzis, K., Kollias, S.: Possibilistic rule evaluation: a case study in facial expression analysis. Int. J. Fuzzy Syst. 8 (2006)
Lin, C.-T., Fan, K.-W., Yeh, C.-M., Pu, H.-C., Wu, F.-Y.: High-accuracy skew estimation of document images. Int. J. Fuzzy Syst. 8 (2006)
Gomathi, C., Rajamani, V.: Skill-based education through fuzzy knowledge modeling for e-learning. Comput. Appl. Eng. Educ. 26, 393–404 (2018). https://doi.org/10.1002/cae.21892
Goyal, M., Yadav, D., Choubey, A.: Fuzzy logic approach for adaptive test sheet generation in e-learning. In: 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), pp. 1–4 (2012)
Deborah, L.J., Sathiyaseelan, R., Audithan, S., Vijayakumar, P.: Fuzzy-logic based learning style prediction in e-learning using web interface information. Sadhana 40, 379–394 (2015). https://doi.org/10.1007/s12046-015-0334-1
Cavus, N.: The evaluation of learning management systems using an artificial intelligence fuzzy logic algorithm. Adv. Eng. Softw. 41, 248–254 (2010). https://doi.org/10.1016/j.advengsoft.2009.07.009
Tsaganou, G., Grigoriadou, M., Cavoura, T., Koutra, D.: Evaluating an intelligent diagnosis system of historical text comprehension. Expert Syst. Appl. 25, 493–502 (2003). https://doi.org/10.1016/s0957-4174(03)00090-3
Kosba, E., Dimitrova, V., Boyle, R.: Using fuzzy techniques to model students in web-based learning environments. In: Palade, V., Howlett, R.J., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, pp. 222–229. Springer, Heidelberg (2003)
Hsieh, T.-C., Wang, T.-I., Su, C.-Y., Lee, M.-C.: A fuzzy logic-based personalized learning system for supporting adaptive english learning. J. Educ. Technol. Soc. 15, 273–288 (2012)
Guimarães, R. dos S., Strafacci, V., Tasinaffo, P.M.: Implementing fuzzy logic to simulate a process of inference on sensory stimuli of deaf people in an e-learning environment. Comput. Appl. Eng. Educ. 24, 320–330 (2016). https://doi.org/10.1002/cae.21707
Xu, D., Wang, H., Su, K.: Intelligent student profiling with fuzzy models. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences. pp. 8 (2002)
Kavcic, A.: Fuzzy student model in InterMediActor platform. In: 26th International Conference on Information Technology Interfaces, 2004. pp. 297–302, vol. 1 (2004)
Salim, N., Haron, N.: The construction of fuzzy set and fuzzy rule for mixed approach in adaptive hypermedia learning system. In: Pan, Z., Aylett, R., Diener, H., Jin, X., Göbel, S., and Li, L. (eds.) Technologies for e-Learning and Digital Entertainment. pp. 183–187. Springer, Heidelberg (2006)
Bradac, V., Walek, B.: A comprehensive adaptive system for e-learning of foreign languages. Expert Syst. Appl. 90, 414–426 (2017). https://doi.org/10.1016/j.eswa.2017.08.019
Popescu, E., Badica, C., Moraret, L.: Accommodating learning styles in an adaptive educational system. Informatica 34 (2010)
Shakouri G., H., Menhaj, M.B.: A systematic fuzzy decision-making process to choose the best model among a set of competing models. IEEE Trans. Syst. Man Cybern. - Part Syst. Hum. 38, 1118–1128 (2008). https://doi.org/10.1109/tsmca.2008.2001076
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ennouamani, S., Mahani, Z. (2019). Towards Adaptive Learning Systems Based on Fuzzy-Logic. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-22871-2_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22870-5
Online ISBN: 978-3-030-22871-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)