Skip to main content

Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results

  • Conference paper
Towards Ubiquitous Learning (EC-TEL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6964))

Included in the following conference series:

  • 2340 Accesses

Abstract

The actions a user performs on exercises depending on the different hinting techniques applied, can be used to adapt future exercises. In this paper, we propose a survey for users in order to know their different actions depending on different conditions. The analysis of preliminary results for some questions of the model shows that there is a correlation between some survey questions and the real student actions, but there is a case in which there is not such correlation. For the cases where that correlation exists, this correlation leads to think that some prediction of users actions based on survey results is possible.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

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.

Similar content being viewed by others

References

  1. Gertner, A.S., Conati, C., Vanlehn, K.: Procedural help in Andes: Generating hints using a Bayesian network student model. In: 15th National Conference on Artificial Intelligence, pp. 106–111. AAAI Press, Menlo Park (1998)

    Google Scholar 

  2. Conejo, R., Guzmán, E., de-la Cruz, J.-L.P., Millán, E.: An Empirical Study About Calibration of Adaptive Hints in Web-Based Adaptive Testing Environments. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 71–80. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Zhoul, Y., Freedman, R., Glass, M., Michael, J.A., Rovick, A., Evens, M.W.: Delivering hints in a dialogue-based intelligent tutoring system. In: 16th National Conference on Artificial intelligence, pp. 128–134. AAII (1999)

    Google Scholar 

  4. Muñoz-Merino, P.J., Delgado Kloos, C., Muñoz-Organero, M.: Deciding on Different Hinting Techniques in Assessments for Intelligent Tutoring Systems. International Journal of Innovative Computing Information and Control 7(2), 841–858 (2011)

    Google Scholar 

  5. Feng, M., Heffernan, N.T., Koedinger, K.R.: Predicting state test scores better with intelligent tutoring systems: developing metrics to measure assistance required. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 31–40. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  6. Baker, R., Corbett, A.T., Roll, I., Koedinger, K.R.: Developing a generalizable detector of when students game the system. User Mod. and User-Adapted Int. 18, 287–314 (2008)

    Article  Google Scholar 

  7. Aleven, V., Mclaren, B., Roll, I., Koedinger, K.R.: Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor. Int. J. Artif. Intell. 16, 101–128 (2006)

    Google Scholar 

  8. Feng, M., Heffernan, N.T., Beck, J.: Using learning decomposition to analyze instructional effectiveness in the ASSISTment system. In: 14th International Conference on Artificial Intelligence in Education (AIED 2009), pp. 523–530. IOS Press, Amsterdam (2009)

    Google Scholar 

  9. Wolpers, M., Najjar, J., Verbert, K., Duval, E.: Tracking Actual Usage: the Attention Metadata Approach. Educational Technology & Society 10(3), 106–121 (2007)

    Google Scholar 

  10. Schmitz, H., Scheffel, M., Friedrich, M., Jahn, M., Niemann, K., Wolpers, M.: CAMera for PLE. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 507–520. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  11. Vasilyeva, E., Pechenizkiy, M., De Bra, P.M.E.: Adaptation of Elaborated Feedback in e-Learning. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 235–244. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Felder, R.M., Silverman, L.K.: Learning and Teaching Styles in Engineering Education. Journal of Engineering Education 78, 674–681 (1988)

    Google Scholar 

  13. Mori, J., Matsuo, Y., Koshiba, H., Aihara, K., Takeda, H.: Predicting Customer Models Using Behavior-Based Features in Shops. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 126–137. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Bohnert, F., Zukerman, I., Berkovsky, S., Baldwin, T., Sonenberg, L.: Using Interest and Transition Models to Predict Visitor Locations in Museums. AI Communications 21 (AICom) – Special Issue on Recommender Systems, 195–202 (2008)

    Google Scholar 

  15. Malhotra, N.K.: Questionnaire design and scale development. In: The Handbook of Marketing Research Uses Misuses and Future Advances, pp. 83–94. Sage Publications, Inc., Thousand Oaks (2006)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Muñoz-Merino, P.J., Pardo, A., Muñoz-Organero, M., Delgado Kloos, C. (2011). Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds) Towards Ubiquitous Learning. EC-TEL 2011. Lecture Notes in Computer Science, vol 6964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23985-4_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23985-4_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23984-7

  • Online ISBN: 978-3-642-23985-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics