Monitoring User Patterns in School Information Systems Using Logfile Analysis

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 400)


Analyzing user patterns in school information systems can be difficult as several methods (e.g. interviews, surveys, and observations) can be time-consuming. We propose logfile analysis as a method that offers several advantages, primarily non-reactive data capture. With logfiles from a school with over 100 teachers over a seven month period, we try to get a deeper insight about the system’s usage and the interactions between users. The results show that three user groups can be identified, classified by the intensity of usage. Network graphs helped us to visualize a complex system and helped us to identify important subjects and categories. Nevertheless, logfiles alone lack in providing information giving deeper insights about uses of the system like user goals and aims.


Data mining web mining school information systems logfile analysis 


  1. 1.
    Breiter, A., Lange, A., Stauke, E. (eds.): School Information Systems and Data-based Decision-Making. Schulinformationssysteme und datengestützte Entscheidungsprozesse. Peter Lang, Frankfurt-am-Main (2008)Google Scholar
  2. 2.
    Hew, K.F., Brush, T.: Integrating Technology into K-12 Teaching and Learning: Current Knowledge Gaps and Recommendations for Future Research. Educational Technology Research and Development 55(3), 223–252 (2007)CrossRefGoogle Scholar
  3. 3.
    Pelgrum, W.J.: Obstacles to the integration of ICT in education: results from a worldwide educational assessment. Computers & Education 37(2), 163–178 (2001)CrossRefGoogle Scholar
  4. 4.
    Breiter, A., Welling, S., Schulz, A.H.: Mediatisierung schulischer Organisationskulturen. In: Hepp, A., Krotz, F. (eds.) Mediatisierte Welten: Beschreibungsansätze und Forschungsfelder, pp. 96–117. VS Verlag, Wiesbaden (2011)Google Scholar
  5. 5.
    Schulz, W.: Reconstructing Mediatization as an Analytical Concept. European Journal of Communication 19(1), 87–101 (2004)CrossRefGoogle Scholar
  6. 6.
    Oliner, A., Ganapathi, A., Xu, W.: Advances and challenges in log analysis. Communications of the ACM 55(2), 55–61 (2012)CrossRefGoogle Scholar
  7. 7.
    Suneetha, K.R., Krishnamoorthi, R.: Identifying User Behavior by Analyzing Web Server Access Log File. IJCSNS International Journal of Computer Science and Network Security 9(4), 327–332 (2009)Google Scholar
  8. 8.
    Markov, Z., Larose, D.T.: Data mining the web: uncovering patterns in web content, structure and usage. Wiley, Hoboken (2007)Google Scholar
  9. 9.
    Büchner, A.G., Mulvenna, M.D.: Discovering Internet Marketing Intelligence through Online Analytical Web Usage Mining. ACM SIGMOD Record 27(4), 54–61 (1998)CrossRefGoogle Scholar
  10. 10.
    Song, Q., Shepperd, M.: Mining web browsing patterns for E-commerce. Computers in Industry 57(7), 622–630 (2006)CrossRefGoogle Scholar
  11. 11.
    Larose, D.T.: Data Mining Methods and Models. Wiley-IEEE Press, Hoboken (2006)zbMATHGoogle Scholar
  12. 12.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. Communications of the ACM 43, 142–151 (2000)CrossRefGoogle Scholar
  13. 13.
    Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds.): WebKDD 2003. LNCS (LNAI), vol. 2703. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: IEEE International Conference on Tools with Artificial Intelligence, p. 558, IEEE Computer Society (1997)Google Scholar
  15. 15.
    Huang, X., An, A., Liu, Y.: Web Usage Mining with Web Logs. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, 2nd edn., pp. 2096–2102. Information Science Reference, Hershey (2008)CrossRefGoogle Scholar
  16. 16.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explor. Newsl. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  17. 17.
    Liu, B.: Web data mining: exploring hyperlinks, contents, and usage data. Springer, Berlin (2011)zbMATHGoogle Scholar
  18. 18.
    Mobasher, B.: Web Mining Overview. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, 2nd edn., pp. 2085–2089. Information Science Reference, Hershey (2008)CrossRefGoogle Scholar
  19. 19.
    Lazar, J., Feng, J.H., Hochheiser, H.: Research Methods in Human-Computer Interaction. Wiley, Chichester (2010)Google Scholar
  20. 20.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  21. 21.
    Banerjee, A., Ghosh, J.: Clickstream Clustering using Weighted Longest Common Subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining (2001)Google Scholar
  22. 22.
    Chen, J., Cook, T.: Mining contiguous sequential patterns from web logs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1177–1178. ACM (2007)Google Scholar
  23. 23.
    Hay, B., Wets, G., Vanhoof, K.: Mining navigation patterns using a sequence alignment method. Knowledge and Information Systems 6(2), 150–163 (2004)Google Scholar
  24. 24.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences (Extended Abstract). In: 1st Conference on Knowledge Discovery and Data Mining, pp. 210–215 (1995)Google Scholar
  25. 25.
    van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering Social Networks from Event Logs. Computer Supported Cooperative Work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  26. 26.
    Hogan, B.: Analyzing Social Networks via the Internet. In: Fielding, N., Lee, R.M., Blank, G. (eds.) The SAGE Handbook of Online Research Methods, pp. 141–160. SAGE, Los Angeles (2008)CrossRefGoogle Scholar
  27. 27.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  28. 28.
    Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the World-Wide web. Computer Networks and ISDN Systems 27(6), 1065–1073 (1995)CrossRefGoogle Scholar
  29. 29.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Software: Practice and Experience 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  30. 30.
    Twisk, J.W.R.: Applied Multilevel Analysis. Cambridge University Press, Cambridge (2007)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  1. 1.Institute for Information ManagementUniversity of BremenGermany

Personalised recommendations