Skip to main content
SpringerLink
Log in

Assessment of Test Items Quality and Adaptive Testing on the Rasch Model

  • Conference paper
  • First Online:
Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1698))

  • 146 Accesses

Abstract

In this paper the algorithm for adaptive testing of students’ knowledge in distance learning and an assessment of its effectiveness in the educational process has been proposed. The results of the study are based on the achievements of modern testing theory IRT. The aim of the study was to build an adaptive testing algorithm that allows you to objectively assess students’ knowledge and assess the quality of test items. To achieve this goal, a mathematical model of modern testing theory IRT, namely the Rasch model, was used. The effectiveness of the proposed algorithm for the objective assessment of students’ knowledge has been experimentally shown. The correspondence of the empirical data to the Rasch model was assessed based on the Pearson’s chi-squared test. The quality of test items was provided based on the Rasch model.

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

Access this chapter

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

References

  1. Al-A’ali, M.: IRT-item response theory assessment for an adaptive teaching assessment system. In: Proceedings of the 10th WSEAS International Conference on Applied Mathematics, pp. 518–522 (2006)

    Google Scholar 

  2. Andrich, D.: The Rasch model explained. In: Maclean, R., et al. (eds.) Applied Rasch Measurement: A Book of Exemplars. EDAP, vol. 4, pp. 27–59. Springer, Dordrecht (2005). https://doi.org/10.1007/1-4020-3076-2_3

  3. Andrich, D.: Rasch Models for Measurement. Thousand Oaks, California (2021). https://doi.org/10.4135/9781412985598, https://methods.sagepub.com/book/rasch-models-for-measurement

  4. Andrich, D., Sheridan, B., Luo, G.: RUMM 2010: Rasch unidimensional measurement models (2001)

    Google Scholar 

  5. Bezruczko, N.: Rasch Measurement in Health Sciences. Jam Press, Maple Grove (2005)

    Google Scholar 

  6. Bond, T., Yan, Z., Heene, M.: Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Routledge, Abingdon (2020)

    Book  Google Scholar 

  7. Brown, W.: Some experimental results in the correlation of mental abilities. Br. J. Psychol. 3, 296–322 (1910). Place: United Kingdom Publisher: British Psychological Society

    Google Scholar 

  8. Choi, Y., McClenen, C.: Development of adaptive formative assessment system using computerized adaptive testing and dynamic Bayesian networks. Appl. Sci. 10(22), 8196 (2020)

    Article  Google Scholar 

  9. Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951). https://doi.org/10.1007/BF02310555

    Article  MATH  Google Scholar 

  10. Eckes, T.: Many-facet Rasch measurement (2009)

    Google Scholar 

  11. Fischer, G.H., Molenaar, I.W.: Rasch Models: Foundations, Recent Developments, and Applications. Springer, New York (2012)

    Google Scholar 

  12. Guilford, J.P.: Fundamental Statistics in Psychology and Education, p. 333. McGraw-Hill, New York (1942)

    Google Scholar 

  13. Gulliksen, H.: Perspective on educational measurement. Appl. Psychol. Meas. 10(2), 109–132 (1986). https://doi.org/10.1177/014662168601000201. Publisher: SAGE Publications Inc

  14. Guttman, L.: A basis for scaling qualitative data. Am. Sociol. Rev. 9, 139–150 (1944). https://doi.org/10.2307/2086306. Place: US Publisher: American Sociological Assn

  15. Ingebo, G.S.: Probability in the Measure of Achievement. ERIC, Ipswich (1997)

    Google Scholar 

  16. Kuder, G.F., Richardson, M.W.: The theory of the estimation of test reliability. Psychometrika 2(3), 151–160 (1937). https://doi.org/10.1007/BF02288391

    Article  Google Scholar 

  17. van der Linden, W.J., Hambleton, R.K.: Handbook of Modern Item Response Theory. Springer, New York (2013)

    Google Scholar 

  18. Lord, F., Novick, M., Birnbaum, A.: Statistical Theories of Mental Test Scores. Addison-Wesley, Oxford (1968)

    MATH  Google Scholar 

  19. Lord, F.M.: Applications of Item Response Theory to Practical Testing Problems. Routledge, Abingdon (1980)

    Google Scholar 

  20. Luce, R.D., Tukey, J.W.: Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psychol. 1, 27 (1964)

    Article  MATH  Google Scholar 

  21. Melesko, J., Novickij, V.: Computer adaptive testing using upper-confidence bound algorithm for formative assessment. Appl. Sci. 9(20), 4303 (2019). https://doi.org/10.3390/app9204303, https://www.mdpi.com/2076-3417/9/20/4303

  22. Melesko, J., Ramanauskaite, S.: Time saving students’ formative assessment: algorithm to balance number of tasks and result reliability. Appl. Sci. 11(13), 6048 (2021)

    Article  Google Scholar 

  23. Muñoz-Merino, P.J., Novillo, R.G., Kloos, C.D.: Assessment of skills and adaptive learning for parametric exercises combining knowledge spaces and item response theory. Appl. Soft Comput. 68, 110–124 (2018)

    Article  Google Scholar 

  24. Perig, V.A., et al.: Engineering pedagogy course mapping. Acta Metallurgica Slovaca 28(1), 49–67 (2022). https://doi.org/10.36547/ams.28.1.1411

  25. Perline, R., Wright, B.D., Wainer, H.: The Rasch model as additive conjoint measurement. Appl. Psychol. Meas. 3(2), 237–255 (1979). Publisher: Sage Publications Sage CA: Thousand Oaks, CA

    Google Scholar 

  26. Rasch, G.: Probabilistic Models for Some Intelligence and Attainment Tests. ERIC, Ipswich (1993)

    Google Scholar 

  27. Sax, G.: Principles of Educational and Psychological Measurement and Evaluation, 3rd edn. Wadsworth Pub. Co., Belmont (1989)

    Google Scholar 

  28. Smith, E.V., Smith, R.M.: Introduction to Rasch Measurement: Theory, Models and Applications. Jam Press, London (2004)

    Google Scholar 

  29. Spearman, C.: Correlation calculated from faulty data. Br. J. Psychol. 1904–1920 3(3), 271–295 (1910). https://doi.org/10.1111/j.2044-8295.1910.tb00206.x. Place: Oxford, UK Publisher: Blackwell Publishing Ltd

  30. Weiss, D.J.: Improving measurement quality and efficiency with adaptive testing. Appl. Psychol. Meas. 6(4), 473–492 (1982). Publisher: Sage Publications Sage CA: Thousand Oaks, CA

    Google Scholar 

  31. Weiss, D.J.: Computerized adaptive testing for effective and efficient measurement in counseling and education. Meas. Eval. Couns. Dev. 37(2), 70–84 (2004). https://doi.org/10.1080/07481756.2004.11909751

    Article  Google Scholar 

  32. Wilson, M.: Constructing Measures: An Item Response Modeling Approach. Routledge, Abingdon (2004)

    Book  Google Scholar 

  33. Wright, B., Linacre, J.: Rasch model derived from objectivity. Rasch Meas. Trans. 1(1), 5–6 (1987)

    Google Scholar 

  34. Wright, B.D.: Solving measurement problems with the Rasch model. J. Educ. Meas. 14, 97–116 (1977). Publisher: JSTOR

    Google Scholar 

  35. Wright, B.D., Masters, G.N.: Rating Scale Analysis. MESA Press, San Diego (1982)

    Google Scholar 

  36. Wright, B.D., Stone, M.H.: Best Test Design. Mesa Press, San Diego (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Donbass State Engineering Academy, Academichna Str. 72, Kramatorsk, 84313, Ukraine

    Alexander Kostikov, Daria Kovalova & Mykyta Zhuravlov

  2. National University of “Kyiv Mohyla Academy”, Kyiv, Ukraine

    Kateryna Vlasenko

  3. Kryvyi Rih State Pedagogical University, Gagarin Ave. 54, Kryvyi Rih, 50086, Ukraine

    Iryna Lovianova

  4. The Institute of Chemical Technologies of the East Ukrainian Volodymyr Dahl National University, Rubizhne, Ukraine

    Sergii Volkov

  5. Limited Liability Company Technical University “Metinvest Polytechnic”, 71-A Sechenov Str., Mariupol, 87524, Ukraine

    Kateryna Vlasenko

Authors
  1. Alexander Kostikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kateryna Vlasenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iryna Lovianova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergii Volkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daria Kovalova
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mykyta Zhuravlov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Kostikov .

Editor information

Editors and Affiliations

  1. Zaporizhzhia National University, Zaporizhzhia, Ukraine

    Vadim Ermolayev

  2. Techforce Infotech Pvt. Ltd., Barcelona, Spain

    David Esteban

  3. University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

    Vitaliy Yakovyna

  4. University of Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  5. V. N. Karazin Kharkiv National University, Kharkiv, Ukraine

    Grygoriy Zholtkevych

  6. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Mykola Nikitchenko

  7. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kostikov, A., Vlasenko, K., Lovianova, I., Volkov, S., Kovalova, D., Zhuravlov, M. (2022). Assessment of Test Items Quality and Adaptive Testing on the Rasch Model. In: Ermolayev, V., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2021. Communications in Computer and Information Science, vol 1698. Springer, Cham. https://doi.org/10.1007/978-3-031-20834-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20834-8_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20833-1

  • Online ISBN: 978-3-031-20834-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation