Advertisement

Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects

  • John Champaign
  • Jie Zhang
  • Robin Cohen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

In this paper, we examine a challenge that arises in the application of peer-based tutoring: coping with inappropriate advice from peers. We examine an environment where students are presented with those learning objects predicted to improve their learning (on the basis of the success of previous, like-minded students) but where peers can additionally inject annotations. To avoid presenting annotations that would detract from student learning (e.g. those found confusing by other students) we integrate trust modeling, to detect over time the reputation of the annotation (as voted by previous students) and the reputability of the annotator. We empirically demonstrate, through simulation, that even when the environment is populated with a large number of poor annotations, our algorithm for directing the learning of the students is effective, confirming the value of our proposed approach for student modeling. In addition, the research introduces a valuable integration of trust modeling into educational applications.

Keywords

Student Learning Recommender System Learning Object Trust Modeling User Modeling 
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.
    McCalla, G.: The ecological approach to the design of e-learning environments: Purpose-based capture and use of information about learners. Journal of Interactive Media in Education 7, 1–23 (2004)Google Scholar
  2. 2.
    Champaign, J., Cohen, R.: A model for content sequencing in intelligent tutoring systems based on the ecological approach and its validation through simulated students. In: Proceedings of FLAIRS-23, Daytona Beach, Florida (2010)Google Scholar
  3. 3.
    Briggs, A.L., Cornell, S.: Self-monitoring Blood Glucose (SMBG): Now and the Future. Journal of Pharmacy Practice 17(1), 29–38 (2004)CrossRefGoogle Scholar
  4. 4.
    Plant, D.: hSITE: healthcare support through information technology enhancements. NSERC Strategic Research Network Proposal (2008)Google Scholar
  5. 5.
    Zhang, J., Cohen, R.: Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach. Electronic Commerce Research and Applications 7(3), 330–340 (2008)CrossRefGoogle Scholar
  6. 6.
    Cheng, R., Vassileva, J.: Design and evaluation of an adaptive incentive mechanism for sustained educational online communities. User Model. User-Adapt. Interact. 16(3-4), 321–348 (2006)CrossRefGoogle Scholar
  7. 7.
    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 (2006)CrossRefGoogle Scholar
  8. 8.
    Brooks, C.A., Panesar, R., Greer, J.E.: Awareness and collaboration in the ihelp courses content management system. In: EC-TEL, pp. 34–44 (2006)Google Scholar
  9. 9.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)CrossRefGoogle Scholar
  10. 10.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering, pp. 43–52. Morgan Kaufmann, San Francisco (1998)Google Scholar
  11. 11.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  12. 12.
    van Labeke, N., Poulovassilis, A., Magoulas, G.D.: Using similarity metrics for matching lifelong learners. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 142–151. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    VanLehn, K., Ohlsson, S., Nason, R.: Applications of simulated students: An exploration. Journal of Artificial Intelligence in Education 5, 135–175 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John Champaign
    • 1
  • Jie Zhang
    • 2
  • Robin Cohen
    • 1
  1. 1.University of WaterlooWaterlooCanada
  2. 2.School of Computer EngineeringSingapore

Personalised recommendations