Studying the Impact of Personality and Group Formation on Learner Performance

  • Víctor Sánchez Hórreo
  • Rosa M. Carro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4715)


This paper presents a study being carried out at the Universidad Autónoma de Madrid to ascertain the influence of the way students are grouped to do collaborative work (regarding intelligence and personality parameters) on the results they get. Data about student’s personality are analysed along with information about group composition and student performance. The results of this analysis are expected to throw light about the impact of personal traits and group formation on learning. This information can be incorporated in collaborative systems as criteria for group formation, with the aim of favouring CSCL situations in which students are prone to get better results.


Group Formation Collaborative Learning Learner Performance Personality Parameter Collaborative Task 
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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  1. 1.
    Carro, R.M., Ortigosa, A., Schlichter, J.: A Rule-based formalism for describing collaborative adaptive courses. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774, pp. 252–259. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Alfonseca, E., Carro, R.M., Paredes, M., Ortigosa, A., Martín, E.: The impact of learning styles on student grouping for collaborative learning: a case study. User Modeling and User-Adapted Interaction. Special Issue: User Modelling to Support Groups, Communities and Collaboration 16(3-4), 377–401 (2006)Google Scholar
  3. 3.
    Rohde, T.E., Thompson, L.A.: Predicting academic achievement with cognitive ability. Intelligence 35(1), 83–92 (2007)CrossRefGoogle Scholar
  4. 4.
    Chamorro-Premuzic, T., Furnham, A.: Personality predicts academia performance: Evidence from two longitudinal examples. Journal of Research in Personality, University College of London 37, 319–338 (2003)Google Scholar
  5. 5.
    Golding, P., Facey-Shaw, L., Tennant, V.: Effects of peer tutoring, attitude and personality on academic performance of first year introductory programming students. In: 36th ASEE/IEEE Frontiers in Education Conference. University of Technology, Jamaica (2006)Google Scholar
  6. 6.
    Johnson, D.W., Johnson, R.T., Smith, K.: Cooperative learning: Increasing college faculty instructional productivity (ASHEERIC Higher Education Report No. 4). The George Washington University, School of Education and Human Development (1991)Google Scholar
  7. 7.
    Dillenbourg, P.: Collaborative Learning: Cognitive and Computational Approaches. Elsevier, Oxford (1999)Google Scholar
  8. 8.
    Younis, N., Salman, R., Ashrafi, R.: Efficacy of present e-learning content to student personality types. International journal of information technology 1(3) (2004)Google Scholar
  9. 9.
    Abrahamian, E., Weinberg, J., Grady, M.S.: Stanton. Saint Louis University.USA: The effect of personality-aware computer-human interfaces on learning. Journal of Universal Computer Science 9(1) (2004)Google Scholar
  10. 10.
    Chen, S., Caropreso, E.: Influence of personality on online discussion. Journal of Interactive Online Learning 3(2) (2004)Google Scholar
  11. 11.
    Johnson, D.W., Johnson, F.P.: Learning together: group theory and group skills. Pearson Education (1975)Google Scholar
  12. 12.
    Deibel, K.: Team formation methods for increasing interaction during in-class group work. In: Proceedings of the 10th annual SIGCSE Conference on Innovation and Technology in Computer Science Education, Caparica, Portugal, pp. 291–295 (2005)Google Scholar
  13. 13.
    Muehlenbrock, M.: Learning group formation based on learner profile and context. Int. J. e-learning IJEL 5(1), 19–24 (2006)Google Scholar
  14. 14.
    Read, T., Barros, B., Bárcena, E., Pancorbo, J.: Coalescing individual and collaborative learning to model user linguistic competences. User Modeling and User-Adapted Interaction 16(3-4), 349–376(28) (2006)Google Scholar
  15. 15.
    Carro, R.M., Ortigosa, A., Martin, E., Schlichter, J.: Dynamic generation of adaptive web-based collaborative courses. In: Favela, J., Decouchant, D. (eds.) CRIWG 2003. LNCS, vol. 2806, pp. 191–198. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Costa, P.T., McCrae, R.R.: The NEO-PI/NEO-FFI manual supplement. Odessa, FL Psychological Assessment Resources (1989)Google Scholar
  17. 17.
    Thurstone, L.L.: Primary Mental Abilities. Chicago University Press, Chicago (1938)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Víctor Sánchez Hórreo
    • 1
  • Rosa M. Carro
    • 1
  1. 1.Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 MadridSpain

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