Skip to main content

Towards Adaptive Learning Systems Based on Fuzzy-Logic

  • Conference paper
  • First Online:
Intelligent Computing (CompCom 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 997))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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

  2. 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)

    Google Scholar 

  3. 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

  4. 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)

    Google Scholar 

  5. 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

  6. 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

  7. 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)

    Google Scholar 

  8. Zhu, Z.T., Bin, H.: Smart education: a new paradigm in educational technology. Telecommun. Educ. 12, 3–15 (2012)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. Tan, H., Guo, J., Li, Y.: E-learning recommendation system. In: 2008 International Conference on Computer Science and Software Engineering. pp. 430–433 (2008)

    Google Scholar 

  12. Cavus, N., Bicen, H., Akcil, U.: The Opinions of Information Technology Students on Using Mobile Learning (2008)

    Google Scholar 

  13. Naismith, L., Lonsdale, P., Vavoula, G.N., Sharples, M.: Mobile Technologies and Learning (2004)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. Traxler, J., Kukulska-Hulme, A.: Mobile Learning: A Handbook for Educators and Trainers. Routledge, Abingdon (2007)

    Google Scholar 

  16. 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

  17. Norris, C.A., Soloway, E.: Learning and schooling in the age of mobilism. Educ. Technol. 51, 3–10 (2011)

    Google Scholar 

  18. 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

  19. 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

  20. Kidd, T.T.: Online education and adult learning: new frontiers for teaching practices. Information Science Reference (2010)

    Google Scholar 

  21. 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

  22. 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

  23. 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)

    Google Scholar 

  24. V. J. Shute, D.Z.-R.: Adaptive Educational Systems (2012)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. 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

  27. Moore, M.G.: Editorial: three types of interaction. Am. J. Distance Educ. 3, 1–7 (1989). https://doi.org/10.1080/08923648909526659

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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

  31. 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)

    Google Scholar 

  32. Zadeh, L.A.: Information and control. Fuzzy Sets. 8, 338–353 (1965)

    Google Scholar 

  33. 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

  34. 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

  35. 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)

    Google Scholar 

  36. 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

  37. 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

  38. 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

  39. 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

  40. Aessilan, S., Mamdani, E.: An experiment in linguistic synthesis of fuzzy logic controllers. Int. J. Man-Mach. Stud. 7, 1–13 (1974)

    Google Scholar 

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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)

    Google Scholar 

  46. Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to fuzzy logic using MATLAB. Springer, Berlin (2007)

    Google Scholar 

  47. Wallace, M., Ioannou, S., Karpouzis, K., Kollias, S.: Possibilistic rule evaluation: a case study in facial expression analysis. Int. J. Fuzzy Syst. 8 (2006)

    Google Scholar 

  48. 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)

    Google Scholar 

  49. 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

  50. 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)

    Google Scholar 

  51. 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

  52. 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

  53. 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

  54. 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)

    Google Scholar 

  55. 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)

    Google Scholar 

  56. 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

  57. 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)

    Google Scholar 

  58. Kavcic, A.: Fuzzy student model in InterMediActor platform. In: 26th International Conference on Information Technology Interfaces, 2004. pp. 297–302, vol. 1 (2004)

    Google Scholar 

  59. 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)

    Google Scholar 

  60. 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

  61. Popescu, E., Badica, C., Moraret, L.: Accommodating learning styles in an adaptive educational system. Informatica 34 (2010)

    Google Scholar 

  62. 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

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Soukaina Ennouamani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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

Publish with us

Policies and ethics