Enhancing an Intelligent Tutoring System to Support Student Collaboration: Effects on Learning and Behavior

  • Rachel Harsley
  • Barbara Di Eugenio
  • Nick Green
  • Davide Fossati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331)


In this study we explore how different methods of structuring collaborative interventions affect student learning and interaction in an Intelligent Tutoring System for Computer Science. We compare two methods of structuring collaboration: one condition, unstructured, does not provide students with feedback on their collaboration; whereas the other condition, semistructured, offers a visualization of group performance over time, partner contribution comparison and feedback, and general tips on collaboration. We present a contrastive analysis of student interaction outcomes between conditions, and explore students reported perceptions of both systems. We found that students in both conditions have significant learning gains, equivalent coding efficiency, and limited reliance on system examples. However, unstructured users are more on-topic in their conversational dialogue, whereas semistructured users exhibit better planning skills as problem difficulty increases.


Collaborative intelligent tutoring system Feedback Pair programming Collaboration Data structures CS1 CS2 



This work was supported by the Abraham Lincoln Fellowship 2015–2016 from the University of Illinois at Chicago, and grant NPRP 5–939–1–155 from the Qatar National Research Fund.


  1. 1.
    Harsley, R.: Towards a collaborative intelligent tutoring system classification scheme. In: Proceedings of the 11th International Conference on Cognition and Exploratory Learning in the Digital Age (Celda 2014), Porto, pp. 290–291, October 2014Google Scholar
  2. 2.
    Magnisalis, I., Demetriadis, S., Karakostas, A.: Adaptive and intelligent systems for collaborative learning support: a review of the field. IEEE Trans. Learn. Technol. 4(1), 5–20 (2011)CrossRefGoogle Scholar
  3. 3.
    Olsen, J.K., Aleven, V., Rummel, N.: Adapting collaboration dialogue in response to intelligent tutoring system feedback. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS, vol. 9112, pp. 748–751. Springer, Cham (2015). doi: 10.1007/978-3-319-19773-9_107 CrossRefGoogle Scholar
  4. 4.
    Walker, E., Rummel, N., Koedinger, K.R.: Integrating collaboration and intelligent tutoring data in the evaluation of a reciprocal peer tutoring environment. Res. Pract. Technol. Enhanc. Learn. 04(03), 221–251 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rachel Harsley
    • 1
  • Barbara Di Eugenio
    • 1
  • Nick Green
    • 1
  • Davide Fossati
    • 2
  1. 1.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of Math and Computer ScienceEmory UniversityAtlantaUSA

Personalised recommendations