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Jointly exploring mathematics ability and speed in large-scale computer-based testing

Author

Listed:
  • Luca Bungaro

    (University of Bologna)

  • Marta Desimoni

    (National Institute for the Evaluation of the Education and Training Educational System (INVALSI))

  • Mariagiulia Matteucci

    (University of Bologna)

  • Stefania Mignani

    (University of Bologna)

Abstract

In large-scale tests, the implementation of computer-based testing (CBT) allows to automatically collect data not only on the students’ response accuracy (RA) based on item responses of the test, but also on their response time (RT). RTs can provide a more comprehensive view of a test-taker’s performance beyond just what is obtainable based on correct responses alone. In this paper a joint approach is considered to improve the estimation of ability scores involving complex data coming from computer-based test administration. The study focuses on analysing the data of Italian grade 10 mathematics national assessment administered by the National Institute for the Evaluation of the Education and Training System (INVALSI). In addition, a bivariate multilevel regression with speed and ability estimates, obtained by joint model, is developed including individual covariates to evaluate the contribution of individual and contextual variables in predicting test-taking speed and ability. Overall, the main results indicate that mathematics ability and speed are significantly and negatively correlated, and that the hierarchical data structure (students nested into classes) should be taken into account when explaining the dependency of ability and speed on explanatory variables, such as prior achievement, test anxiety, sociodemographic covariates, class compositional variables, school tracks and geographical area.

Suggested Citation

  • Luca Bungaro & Marta Desimoni & Mariagiulia Matteucci & Stefania Mignani, 2024. "Jointly exploring mathematics ability and speed in large-scale computer-based testing," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1429-1450, November.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:5:d:10.1007_s10260-024-00762-0
    DOI: 10.1007/s10260-024-00762-0
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    References listed on IDEAS

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    1. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    2. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
    3. Pau Balart & Matthijs Oosterveen, 2019. "Females show more sustained performance during test-taking than males," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    4. Fox, Jean-Paul & Entink, Rinke Klein & van der Linden, Wilm, 2007. "Modeling of Responses and Response Times with the Package cirt," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i07).
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