Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ubiquitous Computing and Multimedia Applications

UCMA 2010: Ubiquitous Computing and Multimedia Applications pp 63–72Cite as

Rough Sets Approximations for Learning Outcomes

  • Conference paper

  • 486 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 75))

Abstract

Discovering dependencies between students’ responses and their level of mastering of a particular skill is very important in the process of developing intelligent tutoring systems. This work is an approach to attain a higher level of certainty while following students’ learning progress. Rough sets approximations are applied for assessing students understanding of a concept. Consecutive responses from each individual learner to automated tests are placed in corresponding rough sets approximations. The resulting path provides strong indication about the current level of learning outcomes.

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. Aleven, V., Koedinger, K.R.: Limitations of Student Control: Do Student Know when they need help? In: Gauthier, G., Van Lehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 292–303. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  2. Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004)

    Google Scholar 

  3. Ferreira, U.: A Five-valued Logic and a System. Journal of Computer Science and Technology 4(3), 134–140 (2004)

    Google Scholar 

  4. Goodstein, R.L.: Boolean Algebra. Dover Publications, Mineola (2007)

    Google Scholar 

  5. Gradel, E., Otto, M., Rosen, E.: Undecidability results on two-variable logics. Archive of Mathematical Logic 38, 313–354 (1999)

    Article  MathSciNet  Google Scholar 

  6. Guzmàn, E., Conejo, R.: A model for student knowledge diagnosis through adaptive testing. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 12–21. Springer, Heidelberg (2004)

    Google Scholar 

  7. Harper, R.: Correcting computer-based assessments for guessing. Journal of Computer Assisted Learning 19, 2–8 (2003)

    Article  Google Scholar 

  8. Immerman, N., Rabinovich, A., Reps, T., Sagiv, M., Yorsh, G.: The boundary between decidability and undecidability of transitive closure logics. In: Marcinkowski, J., Tarlecki, A. (eds.) CSL 2004. LNCS, vol. 3210. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Koedinger, K.R., McLaren, B.M., Roll, I.: A help-seeking tutor agent. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)

    Google Scholar 

  10. Marek, V.W., Truszczynski, M.: Contributions to the theory of rough sets. Fundamenta Informaticae 39(4), 389–409 (1999)

    MATH  MathSciNet  Google Scholar 

  11. Mayo, M., Mitrovic, A.: Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelligence in Education 12, 124–153 (2001)

    Google Scholar 

  12. Park, C., Kim, M.: Development of a Level-Based Instruction Model in Web-Based Education. In: Luo, Y. (ed.) CDVE 2004. LNCS (LNAI), vol. 3190, pp. 215–221. Springer, Heidelberg (2004)

    Google Scholar 

  13. Pecheanu, E., Segal, C., Stefanescu, D.: Content modeling in Intelligent Instructional Environment. LNCS (LNAI), vol. 3190, pp. 1229–1234. Springer-, Heidelberg (2003)

    Google Scholar 

  14. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  15. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)

    MATH  Google Scholar 

  16. Renkl, A.: Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning and Instruction 12, 529–556 (2002)

    Article  Google Scholar 

  17. Santos, C.T., Osòrio, F.S.: Integrating intelligent agents, user models, and automatic content categorization in virtual environment. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 128–139. Springer, Heidelberg (2004)

    Google Scholar 

  18. Schworm, S., Renkl, A.: Learning by solved example problems: Instructional explanations reduce self-explanation activity. In: Gray, W.D., Schunn, C.D. (eds.) Proceeding of the 24th Annual Conference of the Cognitive Science Society, pp. 816–821. Erlbaum, Mahwah (2002)

    Google Scholar 

  19. Whitesitt, J.E.: Boolean Algebra and Its Applications. Dover Publications, NewYork (1995)

    Google Scholar 

  20. Wood, D.: Scaffolding, contingent tutoring, and computer-supported learning. International Journal of Artificial Intelligence in Education 12, 280–292 (2001)

    Google Scholar 

  21. Yao, Y.Y.: Interval-set algebra for qualitative knowledge representation. In: Proceedings of the Fifth International Conference on Computing and information, pp. 370–374 (1993)

    Google Scholar 

  22. http://www.leeds.ac.uk/perception/v4_mrq.html

  23. Apache HTTP Server Project, http://httpd.apache.org/

  24. Python Programming Language, http://www.python.org/

  25. SQLite, http://www.sqlite.org/

Download references

Author information

Authors and Affiliations

  1. Stord/Haugesund University College, Bjørnsonsg, 45, 5528, Haugesund, Norway

    Sylvia Encheva

  2. IT-Dept., University of Bergen, P. O. Box 7800, 5020, Bergen, Norway

    Sharil Tumin

Authors
  1. Sylvia Encheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sharil Tumin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. VITM, Indore, India

    G. S. Tomar

  2. Department of Computer and Information Science, University of Michigan – Dearborn, 4901 Evergreen Road, 48128, Dearborn, MI, USA

    William I. Grosky

  3. Hannam University, 306-791, Daejeon, South Korea

    Tai-hoon Kim

  4. Department of Computer Science, Lakehead University, P7B 5E1, Thunder Bay, Ontario, Canada

    Sabah Mohammed

  5. Computer Science and Engineering Department, Jadavpur University, Kolkata, India

    Sanjoy Kumar Saha

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Encheva, S., Tumin, S. (2010). Rough Sets Approximations for Learning Outcomes. In: Tomar, G.S., Grosky, W.I., Kim, Th., Mohammed, S., Saha, S.K. (eds) Ubiquitous Computing and Multimedia Applications. UCMA 2010. Communications in Computer and Information Science, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13467-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13467-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13466-1

  • Online ISBN: 978-3-642-13467-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation