Skip to main content

Using the Ecological Approach to Create Simulations of Learning Environments

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7926))

Abstract

Simulated pedagogical agents have a long history in AIED research. We are interested in simulation from another, less well explored perspective: simulating the entire learning environment (including learners) to inform the system design process. An AIED system designer can carry out experiments in the simulation environment that would otherwise be too costly (or time consuming) with real learners using a real system. We suggest that an architecture called the “ecological approach (EA)”[1] can form the basis for creating such simulations. To demonstrate, we describe how to develop a proof-of-concept simulated ITS prototype, modelled in the EA architecture. We also show how to factor in data from two human subject studies (done for other purposes) to gain a degree of cognitive fidelity. An experiment is carried out with the prototype. The approach is general and can apply to learning systems with a wide variety of “pedagogical styles” (not just ITSs) at various stages of their life cycle. We conclude that simulation is a critically needed methodology in AIED.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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 (2004), http://jime.open.ac.uk/jime/article/view/2004-7-mccalla

  2. Bloom, B.: Taxonomy of Educational Objectives, Cognitive and Affective Domains. Longman Group, United Kingdom (1969)

    Google Scholar 

  3. Bateman, S.: Using Group Interaction History in the Wild. In: The Doctoral Colloquium of the 2010 ACM Conf. Conference on Computer Supported Cooperative Work, Savannah, GA, pp. 523–524 (2010)

    Google Scholar 

  4. Peckham, T., McCalla, G.: Mining Student Behavior Patterns in Reading Comprehension Tasks. In: Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., Stamper, J. (eds.) 5th Int. Conf. on Educational Data Mining, Greece, pp. 87–94 (2012)

    Google Scholar 

  5. Champaign, J.: Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach. Ph.D. Thesis, University of Waterloo, Waterloo, Canada (2012)

    Google Scholar 

  6. VanLehn, K., Ohlsson, S., Nason, R.: Applications of Simulated Students: An Exploration. Int. J. Artificial Intelligence in Education 5, 135–175 (1996)

    Google Scholar 

  7. Chan, T.W.: Learning Companion Systems. In: Frasson, C., Gauthier, G. (eds.) Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education, pp. 6–33. Ablex (1990)

    Google Scholar 

  8. Johnson, L., Rickel, J., Lester, J.: Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments. Int. J. of Artificial Intelligence in Education 11, 47–78 (2000)

    Google Scholar 

  9. Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. Int. J. Artificial Intelligence in Education 18(3), 181–208 (2008)

    Google Scholar 

  10. Graesser, A., Chipman, P., Haynes, B., Olney, A.M.: AutoTutor: An Intelligent Tutoring System with Mixed-Initiative Dialogue. IEEE Transactions on Education 48, 612–618 (2005)

    Article  Google Scholar 

  11. Ohlsson, S., Ernst, A.M., Rees, E.: The Cognitive Complexity of Doing and Learning Arithmetic. Research in Mathematics Education 23, 441–467 (1992)

    Google Scholar 

  12. Matsuda, N., Cohen, W.W., Sewall, J., Lacerda, G., Koedinger, K.R.: Predicting Students Performance with SimStudent that Learns Cognitive Skills from Observation. In: Luckin, R., Koedinger, K.R., Greer, J. (eds.) Proc. 12th Int. Conf. on AIED, pp. 467–476 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Erickson, G., Frost, S., Bateman, S., McCalla, G. (2013). Using the Ecological Approach to Create Simulations of Learning Environments. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39112-5_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39111-8

  • Online ISBN: 978-3-642-39112-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics