Advertisement

Adaptive Peer Review Based on Student Profiles

  • Raquel M. Crespo García
  • Abelardo Pardo
  • Carlos Delgado Kloos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

Intelligent tutoring systems cover a wide range of educational processes. However, in the context of peer review methodology, there is no previous work about adaptation of the process according to the student’s profile. In this paper, a methodology for adaptive peer review is introduced. Experimental application of adaptive peer review through two courses allows to confirm pedagogical benefits with actual students’ results.

Keywords

Collaborative Learning Peer Review Process Peer Review Intelligent Tutoring System Fuzzy Classification 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Topping, K.: Peer Assessment Between Students in Colleges and Universities. Review of Educational Research 68(3) (1998)Google Scholar
  2. 2.
    Calibrated Peer Review at, http://cpr.molsci.ucla.edu
  3. 3.
    Gehringer, E.F.: Electronic peer review and peer grading in computer-science courses. In: Proc. of the Technical Symposium on Computer Science Education, pp. 139–143 (2001)Google Scholar
  4. 4.
    Lin, S.S.J., Liu, E.Z.F., Yuan Web, S.M.: peer review: The learner as both adapter and reviewer. IEEE Transactions on Education 44(3), 246–251 (2001)CrossRefGoogle Scholar
  5. 5.
    Trahasch, S.: From peer assessment towards collaborative learning. In: Proc. Frontiers in Education Conf., Savannah, GA, October 20-23 (2004)Google Scholar
  6. 6.
    Ward, A., Sitthiworachart, J., Joy, M.: Aspects of web-based peer assessment systems for teaching and learning computer programming. In: IASTED Int. Conf. on Web-based Ed. (2004)Google Scholar
  7. 7.
    Gehringer, E.F.: Assignment and quality control of peer reviewers. In: Proc. of the American Society for Engineering Education Annual Conf. (2001)Google Scholar
  8. 8.
    Crespo, R.M., Pardo, A., Delgado-Kloos, C.: An adaptive strategy for peer review. In: Proc. Frontiers in Education Conf. (October 2004)Google Scholar
  9. 9.
    Inaba, A., Mizoguchi, R.: Learners’ roles and predictable educational benefits in collaborative learning an ontological approach to support design and analysis of CSCL. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 285–294. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Crespo, R.M., Pardo, A., Pérez, J.P.S., Kloos, C.D.: An algorithm for peer review matching using student profiles based on fuzzy classification and genetic algorithms. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS, vol. 3533, pp. 685–694. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Raquel M. Crespo García
    • 1
  • Abelardo Pardo
    • 1
  • Carlos Delgado Kloos
    • 1
  1. 1.Departamento de Ingeniería TelemáticaUniversidad Carlos III de MadridSpain

Personalised recommendations