Advertisement

The New Competencies Development Level Expertise Method within Intelligent Automated Educational Systems

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 293)

Abstract

The problem of the competencies development level assessing is considered while working with automated educational systems. It is proposed to use expert approach to mediated assessing of the competencies development level within intelligent educational systems. A technique for obtaining numerical estimates of competences according to the result of the test materials passing (tests solutions used as an example), also their normalization with assessment standards identifying are described. The problem of individualization in learning based on a set of software agents of teacher, student, and tutor is considered.

Keywords

automated training systems automated educational systems expert measurement competence development computer test agents 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anokhin, P.: Biology and Neurophysiology of the Conditioned Reflex and Its Role in Adaptive Behavior. Pergamon, Oxford (1974)Google Scholar
  2. 2.
    Argimon-Pallàs, J., et al.: Study protocol of psychometric properties of the Spanish translation of a competence test in evidence based practice: The Festo test. BMC Health Services Research (2009), doi:10.1186/1472-6963-9-37Google Scholar
  3. 3.
    Brusilovsky, P.: Adaptive and Intelligent Web-based Educational Systems. International Jornal of AI in Education 13, 156–169 (2003)Google Scholar
  4. 4.
    Buchanan, B., Shortliffe, E.: Rule-based Expert Systems. Addison-Wesley, New York (1984)Google Scholar
  5. 5.
    Howard, C., Schenk, K., Discenza, R.: Distance Learning and University Effectiveness: Changing Educational Paradigms for Online Learning, London (2004)Google Scholar
  6. 6.
    Jackson, P.: Introduction to Expert Systems. Addison-Wesley, New York (1998)Google Scholar
  7. 7.
    Khnykin, A., Laletin, N., Uglev, V.: Applying Zheleznogorsk Robotics for Learning Children with Disabilities. In: Bravo, J., Hervás, R., Villarreal, V. (eds.) IWAAL 2011. LNCS, vol. 6693, pp. 167–171. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Kravets, O.: The modern pedagogical technologies analysis of automated individualization training. Psychology, Sociology, Pedagogic 4, 14–17 (2011) (in Russian) Google Scholar
  9. 9.
    Mashbits, E.: Psycho-pedagogical problems of education computerization. Moscow (1988) (in Russian)Google Scholar
  10. 10.
    Rauner, F., et al.: Measuring Professional Competence. COMET (2013), doi:10.1007/978-94-007-4725-8_1Google Scholar
  11. 11.
    Schejbal, D.: In Search a New Paradigm of Higher Education. Innovative Higher Education 37, 373–386 (2012), doi:10.1007/s10755-012-9218-zCrossRefGoogle Scholar
  12. 12.
    Shavelson, R.: An Approach to Testing & Modeling Competence. Professional and Vet Learning (2013), doi:10.1007/978-94-6091-867-4_3Google Scholar
  13. 13.
    Uglev, V.: On the specificity of individualization of training in Automated Training Systems. Philosophy of Education 2, 68–74 (2010) (in Russian) Google Scholar
  14. 14.
    Uglev, V.: Intellectual Control Algorithm Interaction improvement by the Users Education Process of the Automation Education Systems. In: International Siberian Conference on Control and Communications (2011), doi:10.1109/SIBCON.2011.6072615Google Scholar
  15. 15.
    Uglev, V.: The Cognitive Maps of Knowledge Diagnosis. Open and Distance Learning 48, 17–23 (2012) (in Russian) Google Scholar
  16. 16.
    Uglev, V.: The specificity of operator training on the basis of processfocused on model Automated Educational Systems. Questions of modern science and practice. Vernadsky University 48, 54–58 (2013) (in Russian)Google Scholar
  17. 17.
    Uglev, V., Samrina, F.: Using of possibilities in Learning Tests for individualization of displaying material in Electronic Education Courses. Modern Techniques and Technologies (2008), doi:10.1109/SPCMTT.2008.4897509Google Scholar
  18. 18.
    Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine, New York (1948)Google Scholar
  19. 19.
    Woloszynski, T., Kurzynski, M.: On a New Measure of Classifier Competence in the Feature Space. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 285–292. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Zvonnikov, V., Chelyshkova, M.: Quality control training for certification: competence approach, Moscow (2009) (in Russian)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Center for Applied Research of Siberian Federal UniversityZheleznogorskRussia

Personalised recommendations