Advertisement

Reusability of Data Flow Designs in Complex CSCL Scripts: Evaluation Results from a Case Study

  • Osmel Bordiés
  • Eloy Villasclaras
  • Yannis Dimitriadis
  • Adolfo Ruiz-Calleja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7493)

Abstract

Several approaches have addressed the consistency and automatic enactment dimensions of CSCL scripts with data flow, but they have not appropriately tackled the problem of reusing such learning designs. For instance, workflow-based solutions such as LeadFlow4LD only capture particular case behaviors, instead of describing generic data flow situations. This limitation hinders the reusability of these designs because the workflow needs to be adapted for specific technical, teaching and social contexts. This adaptation is complex and time consuming, especially with a large number of students. In order to show the relevance of this problem, this paper analyzes the LeadFlow4LD approach through a real-world complex CSCL script. The study characterizes the reuse effort of CSCL scripts with and without data flow definition, in different social context settings. The findings illustrate how the data flow representation may affect the particularization of complex CSCL scripts, and pave the path for alternative, higher abstraction level representations of data flows, to reduce the reuse effort.

Keywords

reusability data flow complex CSCL scripts workflow 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alvino, S., Asensio-Pérez, Dimitriadis.Y., Hernández-Leo, D.: Supporting the Reuse of Effective CSCL Learning Designs through Social Structure Representations. Distance Education 30(2), 239–258 (2009) Google Scholar
  2. 2.
    Bordiés, O., Dimitriadis, Y., Alario-Hoyos, C., Ruiz-Calleja, A., Subert, A.: Reuse of Data Flow Designs in Complex and Adaptive CSCL Scripts: A Case Study. In: Daradoumis, T., Demetriadis, S.N., Xhafa, F. (eds.) Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative. SCI, vol. 408, pp. 3–28. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Bote-Lorenzo, M.L., Hernández-Leo, D., Dimitriadis, Y.A., Asensio-Pérez, J.I., Vega-Gorgojo, G., Vaquero-González, L.M.: Toward Reusability and Tailorability in Collaborative Learing Systems using IMS-LD and Grid Services. Advanced Learning Technologies 1(3), 129–138 (2004)Google Scholar
  4. 4.
    Burgos, D., Koper, R.: Practical pedagogical uses of IMS Learning Design’s Level B. In: SIGOSSEE 2005, pp. 1–8. Heerlen, The Netherland (2005)Google Scholar
  5. 5.
    Delorme, A.: Statistical Methods. In: Encyclopedia of Medical Device and Instrumentation, vol. 6. Wiley Interscience (2006)Google Scholar
  6. 6.
    Dillenbourg, P., Hong, F.: The Mechanics of CSCL Macro Scripts. International Journal of Computer-Supported Collaborative Learning 3(1), 5–23 (2008)CrossRefGoogle Scholar
  7. 7.
    Gil, Y., Ratnakar, V., Kim, J., Antonio González-Calero, P.A., Groth, P., Moody, J., Deelman, E.: Wings: Intelligent Workflow based Design of Computational Experiments. IEEE Intelligent Systems 26(1), 62–72 (2011)Google Scholar
  8. 8.
    Mendling, J.: Metrics for Business Process Models. In: W. Aalst, J. Mylopoulos, M. Rosemann, M.J. Shaw, C. Szyperski (eds.) Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. LNBIP, vol. 6, ch. 4, pp. 103–133. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Miao, Y., Burgos, D., Griffiths, D., Koper, R.: Representation of Coordination Mechanisms in IMS Learning Design to Support Group-based Learning. In: Lockyer, L., Bennet, S., Agostinho, S., Harper, B. (eds.) Handbook of Research on Learning Design and Learning Objects: Issues, Applications and Technologies, pp. 330–351. IDEA group (2008)Google Scholar
  10. 10.
    Palomino-Ramírez, L., Bote-Lorenzo, M.L., Asensio-Pérez, J.I., Dimitriadis, Y.A.: LeadFlow4LD: Learning and Data Flow Composition-Based Solution for Learning Design in CSCL. In: Briggs, R.O., Antunes, P., de Vreede, G.-J., Read, A.S. (eds.) CRIWG 2008. LNCS, vol. 5411, pp. 266–280. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Palomino-Ramírez, L., Bote-Lorenzo, M., Asensio-Pérez, J., de la Fuente-Valentín, L., Dimitriadis, Y.: The Data Flow Problem in Learning Design: A Case Study. In: Proceedings of the 2008 8th International Conference on Networked Learning, NLC 2008, Halkidiki, Greece, pp. 285–292 (2008)Google Scholar
  12. 12.
    Suri, P.K., Garg, N.: Software Reuse Metrics: Measuring Component Independence and its Applicability in Software Reuse. International Journal of Computer Science and Network Security 9(5), 237–248 (2009)Google Scholar
  13. 13.
    Vignollet, L., Ferraris, C., Martel, C., Burgos, D.: A Transversal Analysis of Different Learning Design Approaches. Journal of Interactive Media in Education (JIME), Special Issue: Comparing Educational Modelling Languages on the “Planet Game” Case Study (2), 1–10 (2008)Google Scholar
  14. 14.
    Villasclaras-Fernández, E.D., Hernández-Leo, D., Asensio-Pérez, J., Dimitriadis, Y., de la Fuente-Valentín, L.: Interrelating Assessment and Flexibility in IMS-LD CSCL Scripts. In: Proceedings of the 8th International Conference on Computer Supported Collaborative Learning, CSCL 2009, pp. 39–43. University of Aegean, Rhodes (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Osmel Bordiés
    • 1
  • Eloy Villasclaras
    • 1
  • Yannis Dimitriadis
    • 1
  • Adolfo Ruiz-Calleja
    • 1
  1. 1.GSIC/EMICUniversity of ValladolidSpain

Personalised recommendations