Using Collaboration Strategies to Support the Monitoring of Online Collaborative Learning Activity

  • Thanasis Daradoumis
  • Angel A. Juan
  • Fernando Lera-López
  • Javier Faulin
Part of the Communications in Computer and Information Science book series (CCIS, volume 73)


This paper first discusses the importance of online education and highlights its main benefits and challenges. In this context, on the one hand, we argue the significance of monitoring students’ and groups’ activity in an online learning environment. On the other hand, we analyze the informational needs that should be covered by any monitoring information system. Finally, the paper goes a step further by proposing the use of collaboration strategies as a manner to improve monitoring and learning processes in computer-supported collaborative learning.


Monitoring Online Learning Collaboration Strategies Computer-Supported Collaborative Learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Daradoumis, T., Xhafa, F., Juan, A.A.: A framework for assessing self, peer and group performance in e-learning. In: Self, peer, and group assessment in e-learning, pp. 279–294. Idea Group Press (IGI Global), USA (2006)CrossRefGoogle Scholar
  2. 2.
    Dillenbourg, P. (ed.): Collaborative learning. Cognitive and computational approaches. Elsevier Science, Oxford (1999)Google Scholar
  3. 3.
    Dillenbourg, P., Tchounikine, P.: Flexibility in macro-scripts for computer-supported collaborative learning. Journal of Computer Assisted Learning 23(1), 1–13 (2007)CrossRefGoogle Scholar
  4. 4.
    Fernandez, R.: Experiences of collaborative e-learning in preservice teachers. Revista Latinoamericana de Tecnologia Educativa 6(2), 77–90 (2007)Google Scholar
  5. 5.
    Freeman, M., McKenzie, J.: SPARK, a confidential web-based template for self and peer assessment of student teamwork: benefits of evaluation across different subjects. British Journal of Educational Technology 33(5), 551–569 (2002)CrossRefGoogle Scholar
  6. 6.
    Gaytan, J.: Visions shaping the future of online education: Understanding its historical evolution, implications, and assumptions. Online Journal of Distance Learning Administration 10(2), 1–10 (2007)Google Scholar
  7. 7.
    Juan Pérez, A., Daradoumis, T., Faulin, J., Xhafa, F.: SAMOS: A Model for Monitoring Students’ and Groups’ Activity in Collaborative e-Learning. International Journal of Learning Technology (IJLT) 4(1/2), 53–72 (2009)CrossRefGoogle Scholar
  8. 8.
    Juan Pérez, A., Daradoumis, T., Faulin, J., Xhafa, F.: A data analysis model based on control charts to monitor online learning processes. International Journal of Business and Data Mining (IJBIM). Special Issue on “Advances in Intelligent Information Management Systems and Applications” 4(2), 159–174 (2009)Google Scholar
  9. 9.
    Kordaki, M., Siempos, H., Daradoumis, T.: Collaborative learning design within open source e-learning systems: lessons learned from an empirical study. In: Magoulas, G. (ed.) Submitted as a chapter in: E-Infrastructures and Technologies for Lifelong Learning: Next Generation Environments. IGI Global, Hershey (2009)Google Scholar
  10. 10.
    Lowry, R.: Computer-aided self assessment -an effective tool. Chemistry Education Research and Practice 6(4), 198–203 (2005)CrossRefGoogle Scholar
  11. 11.
    Meyen, E.L., Aust, R.J., Bui, Y.N., Isaacson, R.: Assessing and monitoring student progress in an e-learning personnel preparation environment. Teacher education and special education 25(2), 187–198 (2002)CrossRefGoogle Scholar
  12. 12.
    Meyer, D.: Quality in distance education. Focus on online learning. ASHE-ERIC Higher Education Report Series, vol. 29(4). Jossey-Bass, San Francisco (2002)Google Scholar
  13. 13.
    Pla, L.M., Rodriguez, S.V., Fonseca, P., Juan, A.A., Faulin, J.: Learning operations research online: Benefits, challenges, and experiences. International Journal of Simulation and Process Modelling (in press)Google Scholar
  14. 14.
    Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)CrossRefGoogle Scholar
  15. 15.
    Sheard, J., Ceddia, J., Hurst, J., Tuovinen, J.: Inferring student learning behaviour from website interactions: A usage analysis. Journal of Education and Information Technologies 8(3), 245–266 (2003)CrossRefGoogle Scholar
  16. 16.
    Simonson, M., Smaldino, S., Albright, M., Zvacek, S.: Teaching and Learning at a Distance: Foundations of distance education. Prentice Hall, Upper Saddle River (2003)Google Scholar
  17. 17.
    Truluck, J.: Establishing a mentoring plan for improving retention in online graduate degree programs. Online Journal of Distance Learning Administration 10(1), 1–6 (2007)Google Scholar
  18. 18.
    Zaiane, O., Xin, M., Han, J.: Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In: Proceedings of the IEEE Forum on Advances in Digital Libraries Conference, pp. 19–29. IEEE Computer Society, Santa Barbara (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thanasis Daradoumis
    • 1
  • Angel A. Juan
    • 1
  • Fernando Lera-López
    • 2
  • Javier Faulin
    • 2
  1. 1.Computer Science, Multimedia and Telecommunication StudiesOpen University of CataloniaBarcelonaSpain
  2. 2.Dep. of Economics and Dep. of Statistics and ORPublic University of NavarrePamplonaSpain

Personalised recommendations