Advertisement

Predicting Results from Interaction Patterns During Online Group Work

  • Alvaro FigueiraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)

Abstract

Group work is an essential activity during both graduate and undergraduate formation. Although there is a vast theoretical literature and numerous case studies about group work, we haven’t yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student’s contribution towards the whole work. We propose and describe a novel tool to manage and assess individual group. Using the collected interactions from the tool usage we create a model for predicting ill-conditioned interactions which generate alerts. We also describe a functionality to predict the final activity grading, based on the interaction patterns and on an automatic classification of these interactions.

Keywords

Group work Individual assessment Interaction pattern Grading prediction Online tool 

Notes

Acknowledgements

This work is financed by ERDF – European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project «Reminds»/UTAP-ICDT/EEI-CTP/0022/2014.

References

  1. 1.
    Swan, K., Shen, J., Hiltz, S.R.: Assessment and collaboration in online learning. J. Asynchronous Learn. Netw. 10(1), 45–61 (2006)Google Scholar
  2. 2.
    Burnett, B., Roberts, A.: Online collaborative assessment: unpacking process and product. In: Comeaux, P. (ed.) Assessing Online Learning, pp. 55–71. Jossey-Bass, San Francisco (2005)Google Scholar
  3. 3.
    Hoffman, J., Rogelberg, S.: All together now? College students’ preferred project group grading procedures. Group Dyn. Theor. Res. Pract. 5(1), 33–40 (2001)CrossRefGoogle Scholar
  4. 4.
    Chyng, Y.J., Steinfeld, C., Pfaff, B.: Supporting awareness among virtual teams in a web-based collaborative system: the TeamSCOPE system. ACM SIGGROUP Bull. 21(3), 28–34 (2000)CrossRefGoogle Scholar
  5. 5.
    Boud, D., Cohen, R., Sampson, A.J.: Peer learning and assessment. Assess. Eval. High. Educ. 24(4), 413–426 (1999)CrossRefGoogle Scholar
  6. 6.
    Strijos, J.W.: Assessment of (computer-supported) collaborative learning. IEEE Trans. Learn. Technol. 4(1), 59–70 (2011)CrossRefGoogle Scholar
  7. 7.
    Gilies, R.M.: Cooperative Learning: Integrating Theory and Practice. Sage, Beverley Hills (2007)CrossRefGoogle Scholar
  8. 8.
    Lee, E., Chan, C., Aalst, J.: Students assessing their own collaborative knowledge building. Int. J. Comput. Support. Collab. Learn. 1(1), 57–87 (2006)CrossRefGoogle Scholar
  9. 9.
    Schummer, T., Strijos, J., Berkel, T.: Measuring group interaction during CSCL. In: Proceedings of Computer Supported Collaborative Learning: Learning (CSCL 2005), pp. 567–576 (2005)Google Scholar
  10. 10.
    Fidalgo-Blanco, Á., Sein-Echaluce, M.L., García-Peñalvo, F.J., Conde, M.Á.: Using learning analytics to improve teamwork assessment. Comput. Hum. Behav. 47, 149–156 (2015)CrossRefGoogle Scholar
  11. 11.
    Figueira, A.: Orchestrating online group work while assessing individual participations. In: Proceedings of INTED 2015, pp. 2996–3005. Madrid (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CRACS & INESC TECUniversity of PortoPortoPortugal

Personalised recommendations