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Cluster Analysis in Personalized E-Learning Systems

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Intelligent Systems for Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 252))

Abstract

Personalized e-learning system should be tailored into student needs, which usually differ even among learners, who attend the same course and have similar technical skills. In the chapter, it is proposed the system architecture, in which teaching paths as well as proper layouts are adjusted to groups of students with similar preferences, created by application of clustering techniques. Learner models are based on dominant learning style dimensions, according to which students focus on different types of information and show different performances in educational process. Extension of the model by including usability preferences is investigated. There are examined different clustering techniques to obtain groups of the best quality. It is presented the algorithm that will fulfill tutor requirements especially concerning the choice of parameters. Some experimental results for real groups of students and different algorithms are described and discussed.

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References

  1. Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education 13, 156–169 (2003)

    Google Scholar 

  2. Santally, M.I., Alain, S.: Personalisation in web-based learning environments. International Journal of Distance Education Technologies 4, 15–35 (2006)

    Google Scholar 

  3. Stash, N., Cristea, A., De Bra, P.: Authoring of learning styles in adaptive hypermedia: Problems and solutions. In: Proc. WWW Conf., N.Y., pp. 114–123 (2004)

    Google Scholar 

  4. Brusilovsky, P.: Adaptive hypermedia. Use Model. User-Adap. 11, 87–110 (2001)

    Article  MATH  Google Scholar 

  5. Lu, J., Yu, C.S., Liu, C.: Learning style, learning patterns and learning performance in a WebCT-based MIS course. Inform. Manage. 40, 497–507 (2003)

    Article  Google Scholar 

  6. Lee, M.: Profiling students adaptation styles in web-based learning. Comput. Educ. 36, 121–132 (2001)

    Article  Google Scholar 

  7. Beaudoin, M.F.: Learning or Lurking? Tracking the ”Invisible” Online Student. Internet & Higher Educ. 5, 147–155 (2002)

    Article  Google Scholar 

  8. Li, Z., Sun, Y., Liu, M.: A web-based intelligent tutoring system. In: Artificiall Intelligence and Innovations AIAI 2005. IFIP International Federation for Information Processing, vol. 187, pp. 583–591. Springer, Boston (2005)

    Chapter  Google Scholar 

  9. Cha, H.J., Kim, Y.S., Park, S.H., Yoon, T.B., Jung, Y.M., Lee, J.-H.: Learning Styles Diagnosis Based on User Interface Behaviors for Customization of Learning Interfaces in an Intelligent Tutoring System. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 513–524. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Gilbert, J.E., Han, C.Y.: Adapting Instruction in Search of a Significant Difference. J. Netw. Comput. Appl. 22, 149–160 (1999)

    Article  Google Scholar 

  11. Saarikoski, L., Salojärvi, S., Del Corso, D., Ovein, E.: The 3DE: an environment for the development of learner-oriented customized educational packages. In: Proc. of ITHET 2001, Kumamoto (2001)

    Google Scholar 

  12. Triantafillou, E., Pomportsis, A., Georgiadou, E.: AES-CS: Adaptive educational system base on cognitive styles. In: AH Workshop, Malaga, pp. 10–20 (2002)

    Google Scholar 

  13. Gonzalez-Rodriguez, M., Manrubia, J., Vidau, A., Gonzalez-Gallego, M.: Improving accessibility with user-tailored interfaces. Appl. Intell. 30, 65–71 (2009)

    Article  Google Scholar 

  14. Shen, R., Han, P., Yang, F., Yang, Q., Huang, J.: Data mining and case-based reasoning for distance learning. Journal of Distance Education Technologies 1, 46–58 (2003)

    Google Scholar 

  15. Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL. 16th European Conference on Artificial Intelligence, pp. 17–23 (2004)

    Google Scholar 

  16. Tang, T., McCalla, G.: Student modeling for a web-based learning environment: A data mining approach. In: Eighteenth National Conference on Artificial Intelligence, Menlo Park, CA, USA, pp. 967–968 (2002)

    Google Scholar 

  17. Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., He, T.: Research on personalized e-learning system using fuzzy set based clustering algorithm. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 587–590. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Merceron, A., Yacef, K.: Clustering students to help evaluate learning. In: Courtiat, J.-P., Davarakis, C., Villemur, T. (eds.) Technology Enchanced Learning, vol. 171, pp. 31–42. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  19. Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE T. Knowl. Data En. 21, 759–772 (2009)

    Article  Google Scholar 

  20. Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007)

    Article  Google Scholar 

  21. Zakrzewska, D.: Cluster Analysis for Building Personalized E-learning System. Pol. J. Environ. Stud. 16, 330–334 (2007)

    Google Scholar 

  22. Zakrzewska, D.: Using Clustering Technique for Students’ Grouping in Intelligent E-Learning Systems. In: Holzinger, A. (ed.) USAB 2008. LNCS, vol. 5298, pp. 403–410. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  23. Felder, R., Brent, R.: Understanding student differences. J. Eng. Educ. 94, 57–72 (2005)

    Google Scholar 

  24. Graf, S., Kinshuk: Considering learning styles in learning managements systems: investigating the behavior of students in an online course. In: Proc. of the 1st IEEE Int. Workshop on Semantic Media Adaptation and Personalization, Athens (2006)

    Google Scholar 

  25. Rovai, A.P.: The relationships of communicator style, personality-based learning style and classroom community among online graduate students. Internet & Higher Education 6, 347–363 (2003)

    Article  Google Scholar 

  26. Alfonseca, E., Carro, R.M., Martin, E., Ortigosa, A., Paredes, P.: The impact of learning styles on student grouping for collaborative learning: a case study. Use Model. User-Adap. 16, 377–401 (2006)

    Article  Google Scholar 

  27. García, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ. 49, 794–808 (2007)

    Article  Google Scholar 

  28. Xu, D., Wang, H., Su, K.: Intelligent student profiling with fuzzy models. In: Proc. of HICSS 2002, Hawaii (2002)

    Google Scholar 

  29. Viola, S.R., Graf, S., Kinshuk, L.T.: Investigating Relationships within the Index of Learning Styles: a Data Driven Approach. Interactive Technology & Smart Education 4, 7–18 (2007)

    Google Scholar 

  30. Kolb, D.A.: Experiential Learning: Experience as a Source of Learning and Development. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  31. Honey, P., Mumford, A.: The Manual of Learning Styles. Peter Honey, Maidenhead (1986)

    Google Scholar 

  32. Dunn, R., Dunn, K.: Teaching Students Through Their Individual Learning Styles: A Practical Approach. Reston Publishing, Reston (1978)

    Google Scholar 

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

    Google Scholar 

  34. ILS Questionnaire, http://www.engr.ncsu.edu/learningstyles/ilsweb.html

  35. Zakrzewska, D., Wojciechowski, A.: Identifying Students Usability Needs in Collaborative Learning Environments. In: 2008 Conference on Human System Interaction, Krakow, pp. 862–867 (2008)

    Google Scholar 

  36. Marcus, A.: Designing Graphical Interfaces. Unix World (October 1990)

    Google Scholar 

  37. Bauersfeld, P.F., Slater, J.L.: User-Oriented Color Interface Design: Direct Manipulation of Color in Context. In: SIGCHI Conference on Human Factors in Computing Systems: Reaching through Technology, New Orleans, pp. 417–418 (2001)

    Google Scholar 

  38. Romero, C., Ventura, S., Garcia, E.: Data mining in course management systems: Moodle case study and tutorial. Comput. Educ. 51, 368–384 (2008)

    Article  Google Scholar 

  39. Han, J., Kamber, M.: Data Mining. Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2006)

    Google Scholar 

  40. Zakrzewska, D., Ruiz-Esteban, C.: Cluster analysis for students profiling. In: Proc. 11th Int. Conf. SMC, pp. 333–338. EXIT, Warsaw (2005)

    Google Scholar 

  41. Jiang, M.F., Tseng, S.S., Su, M.M.: Two-phase clustering process in outliers detection. Pattern Recogn. Lett. 22, 691–700 (2001)

    Article  MATH  Google Scholar 

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

    MATH  Google Scholar 

  43. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density- based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, pp. 226–231 (1996)

    Google Scholar 

  44. Heffler, B.: Individual learning style and the learning style inventory. Educ. Stud. 27, 307–316 (2001)

    Article  Google Scholar 

  45. Hochbaum, S.D., Shmoys, B.D.: A best possible heuristic for the k-center problem. Math. Oper. Res. 10, 180–184 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  46. Dasgupta, S.: Performance guarantees for hierarchical clustering. In: Kivinen, J., Sloan, R.H. (eds.) COLT 2002. LNCS (LNAI), vol. 2375, pp. 235–254. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  47. Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004)

    Article  MATH  Google Scholar 

  48. Tang, J., Chen, Z., Fu, A.W., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535–548. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  49. Zakrzewska, D.: Cluster Analysis for Users’ Modeling in Intelligent E-learning Systems. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 209–214. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  50. Zha, Y., Yu, J.X., Hou, J.: Web Communities. Analysis and Construction. Springer, Heidelberg (2006)

    Google Scholar 

  51. Wang, L.: On competitive learning. IEEE T. Neural Networ. 8, 1214–1217 (1997)

    Article  Google Scholar 

  52. Gower, J.: A general coefficient of similarity and some of its properties. Biometrics 27, 857–874 (1971)

    Article  Google Scholar 

  53. Gan, G., Ma, Ch., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM. SIAM, Philadelphia (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Technical University of Lodz, Wolczanska 215, 90-924, Lodz, Poland

    Danuta Zakrzewska

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  1. Danuta Zakrzewska
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Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wypsianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

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Zakrzewska, D. (2009). Cluster Analysis in Personalized E-Learning Systems. In: Nguyen, N.T., Szczerbicki, E. (eds) Intelligent Systems for Knowledge Management. Studies in Computational Intelligence, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04170-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-04170-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04169-3

  • Online ISBN: 978-3-642-04170-9

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