Skip to main content
SpringerLink
Log in
Book cover

International Conference on Current Trends in Theory and Practice of Computer Science

SOFSEM 2012: SOFSEM 2012: Theory and Practice of Computer Science pp 637–648Cite as

Modeling and Predicting Students Problem Solving Times

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7147))

Abstract

Artificial intelligence and data mining techniques offer a chance to make education tailored to every student. One of possible contributions of automated techniques is a selection of suitable problems for individual students based on previously collected data. To achieve this goal, we propose a model of problem solving times, which predicts how much time will a particular student need to solve a given problem. Our model is an analogy of the models used in the item response theory, but instead of probability of a correct answer, we model problem solving time. We also introduce a web-based problem solving tutor, which uses the model to make adaptive predictions and recommends problems of suitable difficulty. The system already collected extensive data on human problem solving. Using this dataset we evaluate the model and discuss an insight gained by an analysis of model parameters.

This work is supported by GAČR grant No. P202/10/0334.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, J.R., Boyle, C.F., Reiser, B.J.: Intelligent tutoring systems. Science 228(4698), 456–462 (1985)

    Article  Google Scholar 

  2. Baker, F.B.: The basics of item response theory. University of Wisconsin (2001)

    Google Scholar 

  3. Csikszentmihalyi, M.: Beyond boredom and anxiety. Jossey-Bass (1975)

    Google Scholar 

  4. Csikszentmihalyi, M.: Flow: The psychology of optimal experience. HarperPerennial, New York (1991)

    Google Scholar 

  5. De Ayala, R.J.: The theory and practice of item response theory. The Guilford Press (2008)

    Google Scholar 

  6. Jarušek, P., Pelánek, R.: What determines difficulty of transport puzzles? In: Proc. of Florida Artificial Intelligence Research Society Conference, FLAIRS (2011)

    Google Scholar 

  7. Kantor, P.B., Ricci, F., Rokach, L., Shapira, B.: Recommender systems handbook. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  8. Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8(1), 30–43 (1997)

    Google Scholar 

  9. Koedinger, K.R., Corbett, A.T., Ritter, S., Shapiro, L.: Carnegie Learning’s Cognitive Tutor: Summary research results. White paper. Available from Carnegie Learning Inc., 1200 (2000)

    Google Scholar 

  10. Kotovsky, K., Hayes, J.R., Simon, H.A.: Why are some problems hard? Evidence from tower of Hanoi. Cognitive psychology 17(2), 248–294 (1985)

    Article  Google Scholar 

  11. Kotovsky, K., Simon, H.A.: What Makes Some Problems Really Hard: Explorations in the Problem Space of Difficulty. Cognitive Psychology 22(2), 143–183 (1990)

    Article  Google Scholar 

  12. Pelánek, R.: Difficulty rating of sudoku puzzles by a computational model. In: Proc. of Florida Artificial Intelligence Research Society Conference, FLAIRS (2011)

    Google Scholar 

  13. Pizlo, Z., Li, Z.: Solving combinatorial problems: The 15-puzzle. Memory and Cognition 33(6), 1069 (2005)

    Article  Google Scholar 

  14. Simon, H.A., Newell, A.: Human problem solving. Prentice-Hall (1972)

    Google Scholar 

  15. Van der Linden, W.J.: A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics 31(2), 181 (2006)

    Article  Google Scholar 

  16. Van Der Linden, W.J.: Conceptual issues in response-time modeling. Journal of Educational Measurement 46(3), 247–272 (2009)

    Article  Google Scholar 

  17. Vanlehn, K.: The behavior of tutoring systems. International Journal of Artificial Intelligence in Education 16(3), 227–265 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Jarušek & Radek Pelánek

Authors
  1. Petr Jarušek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radek Pelánek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Informatics and Information Technologies, Institute of Informatics and Software Engineering, Slovak University of Technology in Bratislava, Ilkovičova 3, 842 16, Bratislava 4, Slovakia

    Mária Bieliková

  2. Dept. of Intelligent Systems and Business Informatics, Alpen-Adria-Universität Klagenfurt, Universitätsstr. 65-57, 9020, Klagenfurt, Austria

    Gerhard Friedrich

  3. Department of Computer Science, University of Oxford, UK

    Georg Gottlob

  4. Security Engineering Group, Technische Universität Darmstadt, Hochschulstr. 10, 64289, Darmstadt, Germany

    Stefan Katzenbeisser

  5. University of Illinois at Chicago, Dept. of Math., Stat. and Comp. Sci, 851 S. Morgan Street, Chicago, IL 60607-7045, USA; and University of Szeged, Research Group on Artificial Intelligence of the Hungarian Academy of Sciences, 6701 Szeged, Postafiók, Hungary

    György Turán

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jarušek, P., Pelánek, R. (2012). Modeling and Predicting Students Problem Solving Times. In: Bieliková, M., Friedrich, G., Gottlob, G., Katzenbeisser, S., Turán, G. (eds) SOFSEM 2012: Theory and Practice of Computer Science. SOFSEM 2012. Lecture Notes in Computer Science, vol 7147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27660-6_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27660-6_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27659-0

  • Online ISBN: 978-3-642-27660-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation