Skip to main content

Data-Driven Prediction of the Necessity of Help Requests in ILEs

  • Conference paper
Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5149))

Abstract

This paper discusses the data-driven development of a model which predicts whether a student could answer a question correctly without requesting help. This model contributes to a broader piece of research, the primary goal of which was to predict affective characteristics of students working in ILEs. The paper presents the bayesian network which provides adequate predictions, and discusses how its accuracy is taken into account when the model is integrated in an ILE. Future steps to improve the results are briefly discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Porayska-Pomsta, K., Mavrikis, M., Pain, H.: Diagnosing and acting on student affect: the tutor’s perspective. UMUAI 18(1-2) (2008)

    Google Scholar 

  2. Mavrikis, M.: Modelling Students’ Behaviour and Affective States in ILEs through Educational Data Mining. PhD thesis, The University of Edinburgh (2008)

    Google Scholar 

  3. Weiner, B.: Human motivation: Metaphors, theories, and research. Sage, Thousand Oaks (1992)

    Google Scholar 

  4. Corbett, A., Anderson, J.: Student modeling and mastery learning in a computer-based programming tutor (1992)

    Google Scholar 

  5. Conati, C., Gertner, A., VanLehn, K., Druzdzel, M.: On-line student modeling for coached problem solving using bayesian networks. In: Jameson, A., Paris, C., Tasso, C. (eds.) UM: Proceedings of the Sixth International conference (1997)

    Google Scholar 

  6. Mavrikis, M., Maciocia, A.: WaLLiS: a web-based ILE for science and engineering students studying mathematics. In: AIED 2003, Workshop on Advanced Technologies for Mathematics Education, vol. VIII, pp. 505–513 (2003)

    Google Scholar 

  7. Baker, R.S., Corbett, A., Koedinger, K., Wagner, A.: Off-task behavior in the cognitive tutor classroom: When students “game the system”. In: Proceedings of ACM CHI 2004 Conference on Human Factors in Computing Systems, April 24-29, 2004, pp. 383–390. ACM Press, New York (2004)

    Chapter  Google Scholar 

  8. Bouckaert, R.: Bayesian networks in Weka. Technical report, Computer Science Department University of Waikato (2004)

    Google Scholar 

  9. Verma, T., Pearl, J.: An algorithm for deciding if a set of observed independencies has a causal explanation. In: Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pp. 323–330 (1992)

    Google Scholar 

  10. Yu, L., Huan, L.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: International Conference on Machine Learning (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Wolfgang Nejdl Judy Kay Pearl Pu Eelco Herder

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mavrikis, M. (2008). Data-Driven Prediction of the Necessity of Help Requests in ILEs. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2008. Lecture Notes in Computer Science, vol 5149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70987-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70987-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70984-8

  • Online ISBN: 978-3-540-70987-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics