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An Investigation of User Behavior in Educational Platforms Using Temporal Concept Analysis

  • Sanda-Maria DragoşEmail author
  • Christian Săcărea
  • Diana-Florina Şotropa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10308)

Abstract

In this paper, we focus on the problem of investigating user behavior using conceptual structures distilled from weblogs of an educational e-platform. We define a set of so-called attractors as sets of scales in conceptual time systems and compute user life tracks in order to highlight different types of behaviors. These life tracks can give valuable feedback to the instructor how his students are using the online educational resources, analyzing their behavior and extracting as much knowledge as possible from the log access files. This might also be helpful to analyze the usability of the online educational content, eventually for improving the structure of the platform and to develop new educational instruments.

Keywords

Life tracks Temporal Concept Analysis Web logs analysis Conceptual structures User behavior Attractors 

References

  1. 1.
    Wille, R.: Conceptual landscapes of knowledge: a pragmatic paradigm for knowledge processing. In: Gaul, W., Locarek-Junge, H. (eds.) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 344–356. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Dragos, S.: PULSE extended. In: The Fourth International Conference on Internet and Web Applications and Services, pp. 510–515. IEEE Computer Society, Venice/Mestre, May 2009Google Scholar
  3. 3.
    Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S.: Web usage mining for predicting final marks of students that use moodle courses. Comput. Appl. Eng. Educ. 21(1), 135–146 (2013)CrossRefGoogle Scholar
  4. 4.
    Dragos, S., Halita, D., Sacarea, C.: Behavioral pattern mining in web based educational systems. In: Rozic, N., Begusic, D., Saric, M., Solic, P. (eds.) 23rd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2015, Split, Croatia, 16–18 September 2015, pp. 215–219. IEEE (2015). http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7303284
  5. 5.
    Dragos, S., Sacarea, C.: Analysing the usage of pulse portal with formal concept analysis. Studia Universitatis Babes-Bolyai Series Informatica LVII(3), 65–75 (2012)Google Scholar
  6. 6.
    Dragos, S., Halita, D., Sacarea, C., Troanca, D.: An FCA grounded study of user dynamics through log exploration. Studia Universitatis Babes-Bolyai Series Informatica 2, 82–97 (2014)MathSciNetGoogle Scholar
  7. 7.
    Becker, P., Correia, J.H.: The ToscanaJ suite for implementing conceptual information systems. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS, vol. 3626, pp. 324–348. Springer, Heidelberg (2005). doi: 10.1007/11528784_17 CrossRefGoogle Scholar
  8. 8.
    Dragoş, S., Haliţă, D., Săcărea, C., Troancă, D.: Applying triadic FCA in studying web usage behaviors. In: Buchmann, R., Kifor, C.V., Yu, J. (eds.) KSEM 2014. LNCS, vol. 8793, pp. 73–80. Springer, Cham (2014). doi: 10.1007/978-3-319-12096-6_7 Google Scholar
  9. 9.
    Dragoş, S., Haliţă, D., Săcărea, C.: Attractors in web based educational systems a conceptual knowledge processing grounded approach. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS, vol. 9403, pp. 190–195. Springer, Cham (2015). doi: 10.1007/978-3-319-25159-2_18 CrossRefGoogle Scholar
  10. 10.
    Dragoş, S.-M., Haliţă, D.-F., Săcărea, C.: Distilling conceptual structures from weblog data using polyadic FCA. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds.) ICCS 2016. LNCS, vol. 9717, pp. 151–159. Springer, Cham (2016). doi: 10.1007/978-3-319-40985-6_12 Google Scholar
  11. 11.
    Dragos, S., Halita, D., Troanca, D.: Investigating trend-setters in e-learning systems using polyadic formal concept analysis and answer set programming. In: The 4th International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), New York, USA, pp. 42–48, July 2016Google Scholar
  12. 12.
    Dragos, S., Halita, D., Sacarea, C.: Analysing the effect of changing the educational methods by using FCA. In: 24th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2016, Split, Croatia, 22–24 September 2016, pp. 1–5. IEEE (2016). http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7754733
  13. 13.
    Dieberger, A.: Supporting social navigation on the world wide web. Int. J. Hum.-Comput. Stud. 46(6), 805–825 (1997)CrossRefGoogle Scholar
  14. 14.
    Beydoun, G., Kultchitsky, R., Manasseh, G.: Evolving semantic web with social navigation. Expert Syst. Appl. 32(2), 265–276 (2007)CrossRefGoogle Scholar
  15. 15.
    Gonçalves, B., Ramasco, J.J.: Human dynamics revealed through web analytics. CoRR, vol. abs/0803.4018 (2008). http://arxiv.org/abs/0803.4018
  16. 16.
    Norguet, J., Tshibasu-Kabeya, B., Bontempi, G., Zimányi, E.: A page-classification approach to web usage semantic analysis. Eng. Lett. 14(1), 120–126 (2007)Google Scholar
  17. 17.
    Kosala, R., Blockeel, H.: Web mining research: a survey. SIGKDD Explor. 2(1), 1–15 (2000)CrossRefGoogle Scholar
  18. 18.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  19. 19.
    Macfadyen, L.P., Dawson, S.: Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educ. Technol. Soc. 15(3), 149–163 (2012)Google Scholar
  20. 20.
    Spiliopoulou, M., Faulstich, L.C.: WUM: a tool for web utilization analysis. In: Atzeni, P., Mendelzon, A., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 184–203. Springer, Heidelberg (1999). doi: 10.1007/10704656_12 CrossRefGoogle Scholar
  21. 21.
    Romero, C., Ventura, S., Zafra, A., de Bra, P.: Applying web usage mining for personalizing hyperlinks in web-based adaptive educational systems. Comput. Educ. 53(3), 828–840 (2009)CrossRefGoogle Scholar
  22. 22.
    Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysisof recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)CrossRefGoogle Scholar
  23. 23.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 3(1), 12–27 (2013)Google Scholar
  24. 24.
    Liebowitz, J., Frank, M.: Knowledge Management and e-Learning. CRC Press, Boca Raton (2010)CrossRefGoogle Scholar
  25. 25.
    Jo, I.-H., Park, Y., Kim, J., Song, J.: Analysis of online behavior and prediction of learning performance in blended learning environments. Educ. Technol. Int. 15(2), 71–88 (2014)Google Scholar
  26. 26.
    Dragoş, S.-M.: Why Google analytics cannot be used for educational web content. In: 2011 7th International Conference on Next Generation Web Services Practices (NWeSP), pp. 113–118. IEEE (2011)Google Scholar
  27. 27.
    Wolff, K.E.: Temporal concept analysis. In: ICCS-2001 International Workshop on Concept Lattices-Based Theory, Methods and Tools for Knowledge Discovery in Databases, pp. 91–107. Stanford University, Palo Alto (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sanda-Maria Dragoş
    • 1
    Email author
  • Christian Săcărea
    • 1
  • Diana-Florina Şotropa
    • 1
  1. 1.Babeş-Bolyai UniversityCluj-NapocaRomania

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