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Data Warehouse Technology for E-Learning

  • Marta E. Zorrilla
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 225)

Abstract

E-Learning platforms are gaining popularity and relevance among organizations such as global enterprises, open and distance universities and research institutes. But regrettably these platforms present yet unsolved problems. One of these is that instructors cannot guarantee the success of the learning process because they lack tools with which monitor, assess and measure the performance of students in their virtual courses. Therefore, it is necessary to develop specific tools that help professors to do their work suitably. In this chapter, we show that data warehouse and OLAP technologies are the most suitable ones to build this software application. Likewise we explain the steps for its implementation from its conception up to the user interface development. Lastly, we summarize our experience in the design and implementation of MATEP,Monitoring and Analysis Tool for E-learning Platforms, which is a tool built in the University of Cantabria.

Keywords

Data Warehouse Business Intelligence Fact Table Business Requirement Educational Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Álvarez, E., Zorrilla, M.E.: Orientaciones en el diseño y evaluación de un curso virtual parala enseñanza de aplicaciones informáticas. IEEE-RITA 3(2), 1–10 (2008)Google Scholar
  2. 2.
    Avouris, N., Komis, V., Fiotakis, G., Margaritis, M., Voyiatzaki, G.: Logging of fingertip actions is not enough for analysis of learning activities. In: Proccedings of Workshop Usage Analysis in learning systems (AIED 2005), Amsterdam (2005)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems 1(1) (1999)Google Scholar
  6. 6.
  7. 7.
    Han, J.: Data mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)Google Scholar
  8. 8.
    Harinath, S., Quinn, S.: Professional SQL Server Analysis Services 2005 with MDX. Wiley Publishing Inc., Chichester (2006)Google Scholar
  9. 9.
    Hernández Orallo, J., Ramírez Quintana, M.J., Ferri Ramírez, C.: Introducción a la minería de datos. Pearson Prentice Hall, London (2004)Google Scholar
  10. 10.
    Hu, X., Cercone, N.: A data warehouse/online analytic processing framework for web usage mining and business intelligence reporting. International Journal of Intelligent Systems 19(7), 585–606 (2004)CrossRefGoogle Scholar
  11. 11.
    Inmon, W.H.: Building the Data Warehouse. Willey & Son, Chichester (2002)Google Scholar
  12. 12.
    Jacobson, R.: Microsoft SQL Server 2000 Analysis Services step by step. OLAP Train. Microsoft Press (2000)Google Scholar
  13. 13.
    Kimball, R., et al.: The Data Warehouse Lifecycle Toolkit: Tools and Techniques for Designing, Developing, and Deploying Data Warehouses. John Wiley & Sons, Chichester (1998)Google Scholar
  14. 14.
    Kimball, R., Caserta, J.: The Data Warehouse ETL Toolkit. John Wiley & Sons, Chichester (2002)Google Scholar
  15. 15.
    Kimball, R., Ross, M.: The data warehouse toolkit: the complete guide to dimensional modelling. John Wiley & Sons, Chichester (2002)Google Scholar
  16. 16.
    Luotonen, A.: The common log file format (1995), http://www.w3.org/pub/WWW/
  17. 17.
    Martín Fraile, L.: Monitoring and analysis tool for e-Learning platforms. Final Degree Project directed by Zorrilla Pantaleón, M. University of Cantabria (2007)Google Scholar
  18. 18.
    Mazza, R., Dimitrova, V.: CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human-Computer Studies 65(2), 125–139 (2007)CrossRefGoogle Scholar
  19. 19.
    Merceron, A., Yacef, K.: Tada-ed for educational data mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning 7(1), 267–287 (2005)Google Scholar
  20. 20.
  21. 21.
    Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., Heiner, C.: An Educational Data Mining Tool to Browse Tutor-Student Interactions: Time Will Tell! In: Proc. of Workshop on educational data mining, pp. 15–22 (2005)Google Scholar
  22. 22.
    Mundy, J., Thorthwaite, W., Kimball, R.: The Microsoft Data Warehouse Toolkit: with SQL Server 2005 and the Microsoft Business Intelligence Toolset. Wiley Publishing Inc., Chichester (2006)Google Scholar
  23. 23.
    Pabarskaite, Z., Raudys, A.: A process of knowledge discovery from web log data: systematization and critical review. Journal Intelligent Information Systems 28, 79–114 (2007)CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Romero, C., Ventura, S.: Data mining in E-Learning. Advances in Management Information, vol. 4. WIT Press (2006)Google Scholar
  26. 26.
    Spofford, G., et al.: MDX Solutions: With Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase. Wiley Publishing, Chichester (2006)Google Scholar
  27. 27.
    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
  28. 28.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Prentice Hall, London (2006)Google Scholar
  29. 29.
    Tang, C., McCalla, G.: Smart recommendation for an evolving e-learning system. International Journal on E-Learning 4(1), 105–129 (2005)Google Scholar
  30. 30.
    Thomsen, E.: OLAP Solutions: Building Multidimensional Information Systems, 2nd edn. John Wiley & Sons, Chichester (2002)Google Scholar
  31. 31.
    Witten, I., Frank, E.: Data mining. Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  32. 32.
    Zaïane, O., Xin, M., Han, J.: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. In: Proc. Advances in Digital Libraries, Santa Barbara (1998)Google Scholar
  33. 33.
    Zaïane, O.: Web Usage Mining for a Better Web-Based Learning Environment. In: Proc. of Conference on Advantage Technology for Education, Alberta, Canada (2001)Google Scholar
  34. 34.
    Zaïane, O.: Building a Recommender Agent for e-Learning Systems. In: Proceedings of the International Conference on Computers in Education (ICCE) (2000)Google Scholar
  35. 35.
    Zorrilla, M.E., Menasalvas, E., Marín, D., Mora, E., Segovia, J.: Web usage mining project for improving web-based learning sites. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2005. LNCS, vol. 3643, pp. 205–210. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  36. 36.
    Zorrilla, M., Millán, S., Menasalvas, E.: Data webhouse to support web intelligence in e-learning environments. In: Proc. of the IEEE International Conference on Granular Computing, Beijing, China (2005)Google Scholar
  37. 37.
    Zorrilla, M.E., Marín, D., Álvarez, E.: Towards virtual course evaluation using Web Intelligence. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 392–399. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  38. 38.
    Zorrilla, M.E., Álvarez, E.: MATEP: Monitoring and Analysis Tool for e-Learning Platforms. In: Proc. of the 8th IEEE International Conference on Advanced Learning Technologies, Santander, Spain, July, pp. 611–613 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marta E. Zorrilla
    • 1
  1. 1.Department of Mathematics, Statistics and ComputationUniversity of Cantabria.SantanderSpain

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