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Computational Collective Intelligence pp 441–450Cite as

Fuzzy Logic Based Modeling for Building Contextual Student Group Recommendations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

Abstract

Group learning plays an important role in Web based educational process. Groups of students who are to learn together should be characterized by similar features. However course needs may differ depending on the context of the system usage. Each new student, who intends to join the community, should obtain context-aware recommendation of the group of colleagues matching his preferences. In the paper, using fuzzy logic for modeling students and groups is considered. We propose to describe student characteristics by means of fuzzy sets and to use the possibility-based representation of each group. We assume that context is represented by a vector of weights. Then recommendations for new students are determined by applying pattern matching technique including respective context vector. We examine the presented approach by taking into account learning style dimensions as attributes which characterize student preferences. The method is evaluated on the basis of experimental results obtained for real student data.

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References

  1. Schmidt, A., Winterhalter, C.: User Context Aware Delivery of E-learning Material: Approach and Architecture. J. Univers. Comput. Sci. 10, 38–46 (2004)

    Google Scholar 

  2. Shakouri, H.G., Tavassoli, Y.N.: Implementation of a Hybrid Fuzzy System as a Decision Support Process: A FAHP-FMCDM-FIS Composition. Expert Syst. Appl. 39, 3682–3691 (2012)

    Article  Google Scholar 

  3. Zadeh, L.A.: Fuzzy Sets. Inform. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  4. Myszkorowski, K., Zakrzewska, D.: Using fuzzy logic for recommending groups in E-learning systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) ICCCI 2013. LNCS, vol. 8083, pp. 671–680. Springer, Heidelberg (2013)

    Google Scholar 

  5. Myszkorowski, K., Zakrzewska, D.: Building contextual student group recommendations with fuzzy logic. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds.) RSCTC 2014. LNCS, vol. 8536, pp. 358–365. Springer, Heidelberg (2014)

    Google Scholar 

  6. Dubois, D., Prade, H., Testemale, C.: Weighted Fuzzy Pattern Matching. Fuzzy Set Syst. 28, 313–331 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative Filtering Adapted to Recommender Systems of E-learning. Knowl-Based Syst. 22, 261–265 (2009)

    Article  Google Scholar 

  8. Severac, Z., Devedzic, V., Jovanovic, J.: Adaptive Neuro-Fuzzy Pedagogical Recommender. Expert Syst. Appl. 39, 9797–9806 (2012)

    Article  Google Scholar 

  9. Jovanowic, J., Gasewic, D., Knight, C., Richards, G.: Ontologies for Effective Use of Context in E-learning Settings. Educ. Technol. Soc. 10, 47–59 (2007)

    Google Scholar 

  10. Yang, S.J.H.: Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning. Educ. Technol. Soc. 9, 188–201 (2006)

    Google Scholar 

  11. Das, M.M., Chithralekha, T., SivaSathya, S.: Static Context Model for Context Aware E-learning. Int. J. Eng. Sci. Technol. 2, 2337–2346 (2010)

    Article  Google Scholar 

  12. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 217–253. Springer Science+Business Media (2011)

    Google Scholar 

  13. Muehlenbrock, M.: Formation of learning groups by using learner profiles and context information. In: 12th International Conference AIED 2005, pp. 507–514 (2005)

    Google Scholar 

  14. Christodoulopoulos, C.E., Papanikolaou, K.A.: A group formation tool in an E-learning context. In: 19th IEEE ICTAI 2007, vol. 2, pp. 117–123 (2007)

    Google Scholar 

  15. Wang, J., Li, H., Zhao, H.: The Contextual group recommendation. In: 5th International Conference on Intelligent Networking and Collaborative Systems, pp. 127–131 (2013)

    Google Scholar 

  16. Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 677–702. Springer Science+Business Media (2011)

    Google Scholar 

  17. Zheng, Y., Burke, R., Mobasher, B.: Recommendation with differential context weighting. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 152–164. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  18. de Arriaga, F., El Alami, M., Arriaga, A.: Evaluation of fuzzy intelligent learning systems. In: Mendez-Vilas, A., et al. (eds.) Recent Research Developments in Learning Technologies. Formatex, Badajoz (2005)

    Google Scholar 

  19. Hogo, M.: Evaluation of E-Learners Behavior Using Different Fuzzy Clustering Models: A Comparative Study. International Journal of Computer Science and Information Security 7, 131–140 (2010)

    Google Scholar 

  20. Essalmi, F., Ayed, L., Jemni, M., Kinshuk, Graf, S.: Evaluation of personalization strategies based on fuzzy logic. In: 11th IEEE International Conference on Advanced Learning Technologies, pp. 254–256 (2011)

    Google Scholar 

  21. Lu, J.: A personalized e-learning material recommender system. In: the 2nd International Conference on Information Technology for Application, pp. 374–379 (2004)

    Google Scholar 

  22. Vrettaros, J., Vouros, G.A., Drigas, A.S.: Development of an intelligent assessment system for solo taxonomies using fuzzy logic. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 901–911. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. 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 (2013)

    Google Scholar 

  24. Chrysafiadi, K., Virvou, M.: Fuzzy Logic for Adaptive Instruction in an E-learning Environment for Computer Programming. IEEE T. Fuzzy Syst. 23, 164–177 (2015)

    Article  Google Scholar 

  25. Zadeh, L.A.: Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Set Syst. 1, 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  26. Dubois, D., Prade, H.: The Three Semantics of Fuzzy Sets. Fuzzy Set Syst. 90, 141–150 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  27. Felder, R.M., Silverman, L.K.: Learning and Teaching Styles in Engineering Education. Eng. Educ. 78, 674–681 (1988)

    Google Scholar 

  28. Index of Learning Style Questionnaire. http://www.engr.ncsu.edu/learningstyles/ilsweb.html

  29. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Technology Lodz University of Technology, Lodz, Poland

    Krzysztof Myszkorowski & Danuta Zakrzewska

Authors
  1. Krzysztof Myszkorowski
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  2. Danuta Zakrzewska
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Corresponding author

Correspondence to Danuta Zakrzewska .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Myszkorowski, K., Zakrzewska, D. (2015). Fuzzy Logic Based Modeling for Building Contextual Student Group Recommendations. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_43

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24305-4

  • Online ISBN: 978-3-319-24306-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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