Assessing Group Composition in e-learning According to Vygotskij’s Zone of Proximal Development

  • Maria De Marsico
  • Andrea Sterbini
  • Marco Temperini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8514)


In this paper we build on previous work exploring a formal way to assess the composition of learning groups. We start from our existing framework, designed to provide support to personalization in e-learning environments, comprising an implementation of the Vygotskij Theory of proximal development. In such theory, effective individual learning achievements can be only obtained within the boundaries of a cognitive zone where the learner can proceed without frustration, though with support from teacher and peers. In this endeavor, the individual development cannot disregard social-collaborative educational activities. Previously we gave operative definitions of the Zone of Proximal Development for both single learners and groups; here we aim at assessing the viability of a partition of students in groups over a common task.


Individual Zone of Proximal Development Group Zone of Proximal development Personalized learning path Social collaborative e-learning 


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  1. 1.
    Martens, A.: Modeling of Adaptive Tutoring Processes. In: Ma, Z. (ed.) Web-Based Intelligent e-Learning Systems, pp. 193–215. IGI-Global (2005)Google Scholar
  2. 2.
    Limongelli, C., Sciarrone, F., Vaste, G.: LS-plan: An effective combination of dynamic courseware generation and learning styles in web-based education. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 133–142. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Limongelli, C., Sciarrone, F., Vaste, G.: Personalized e-learning in moodle: The moodle-LS system. J. of E-Learning and Knowledge Society 7(1), 49–58Google Scholar
  4. 4.
    Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F.: A Teacher Model to Speed Up the Process of Building Courses. In: Kurosu, M. (ed.) HCII/HCI 2013, Part II. LNCS, vol. 8005, pp. 434–443. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Limongelli, C., Sciarrone, F., Temperini, M., Vaste, G.: The Lecomps5 Framework for Personalized Web-Based Learning: a Teacher’s Satisfaction Perspective. Computers in Human Behavior 27(4) (2011)Google Scholar
  6. 6.
    Ivanova, M., Popova, A.: Formal and Informal Learning Flows Cohesion in Web 2.0 Environment. Int. J. of Information Systems and Social Change, IJISSC 2(1), 1–15 (2011)CrossRefGoogle Scholar
  7. 7.
    Sterbini, A., Temperini, M.: Learning from Peers: Motivating Students through Reputation Systems. In: Proc. Int. Symp. on Applications and the Internet, SAINT, pp. 305–308. IEEE (2008)Google Scholar
  8. 8.
    Cheng, Y., Ku, H.: An investigation of the effects of reciprocal peer tutoring. Computers in Human Behavior 25 (2009)Google Scholar
  9. 9.
    De Marsico, M., Sterbini, A., Temperini, M.: A strategy to join adaptive and reputation-based social-collaborative e-learning, through the Zone of Proximal Development. Int. Journal of Distance Education Technology, IJDET 11(3), 12–31 (2013)CrossRefGoogle Scholar
  10. 10.
    De Marsico, M., Sterbini, A., Temperini, M.: The Definition of a Tunneling Strategy between Adaptive Learning and Reputation-based Group Activities. In: Proc. 11th IEEE Int. Conf. on Advanced Learning Technologies, ICALT, pp. 498–500 (2011)Google Scholar
  11. 11.
    Kreijns, K., Kirschner, P.A., Jochems, W.: Identifying the pitfalls for social interaction in computer supported collaborative learning environments: a review of the research. Computers in Human Behavior 19, 335–353 (2003)CrossRefGoogle Scholar
  12. 12.
    Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F.: A Teaching-Style Based Social Network for Didactic Building and Sharing. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 774–777. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Vygotskij, L.S.: The development of higher forms of attention in childhood. In: Wertsch, J.V. (ed.) The Concept of Activity in Soviet Psychology, Sharpe, Armonk (1981)Google Scholar
  14. 14.
    De Marsico, M., Sterbini, A., Temperini, M.: A Framework to Support Social-Collaborative Personalized e-Learning. In: Kurosu, M. (ed.) HCII/HCI 2013, Part II. LNCS, vol. 8005, pp. 351–360. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Dougiamas, M., Taylor, P.: Moodle: Using learning communities to create an open source course management system. In: Proc. World Conference on Educational Multimedia, Hypermedia and Telecommunications, vol. 1, pp. 171–178Google Scholar
  16. 16.
    De Marsico, M., Temperini, M.: Average effort and average mastery in the identification of the Zone of Proximal Development. In: Proc. 17th IEEE Int. Conf. on System Theory, Control and Computing, ICSTCC, 6th Int. Workshop on Social and Personal Computing for Web-Supported Learning Communities, SPeL, pp. 651–656 (2013)Google Scholar
  17. 17.
    Chaiklin, S.: The zone of proximal development in Vygotsky’s analysis of learning and instruction. In: Kozulin, A., Gindis, B., Ageyev, V., Miller, S. (eds.) Vygotsky’s Educational Theory in Cultural Context, pp. 39–64. Cambridge University Press (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria De Marsico
    • 1
  • Andrea Sterbini
    • 1
  • Marco Temperini
    • 2
  1. 1.Dept. of Computer ScienceSapienza University of RomeItaly
  2. 2.Dept. of Computer, Control, and Management EngineeringSapienza University of RomeItaly

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