Abstract
In the realm of educational assessment, accurate measurement of students’ knowledge and abilities is crucial for effective teaching and learning. Traditional assessment methods often fall short in providing precise and meaningful insights into students’ aptitudes. However, Item Response Theory (IRT), a psychometric framework, offers a powerful toolset to address these limitations. This article proposes an exploration of IRT’s models and their potential to enhance educational assessment practices.
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References
Baker, B., Kim, H.: Item Response Theory: Parameter Estimation Techniques. CRC Press (2004)
De Ayala, J.: The Theory and Practice of Item Response Theory. The Guilford Press (2009)
Doncheva, J., Melandri, S., Valentini, M.: Technology as a school aid in motor activity. In: EDULEARN21 Proceedings, pp. 955–963 (2021)
Embretson, E.: Cognitive Design Systems in Item Response Theory: A Framework for Test Design. Routledge (2013)
Engelhard, G.: Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences. Routledge (2013)
Hambleton, K., Han, N.: Adapting Educational and Psychological Tests for Cross-Cultural Assessment. Routledge (2013)
Shoilekova, K.: Advantages of data mining for digital transformation of the educational system. In: Silhavy, R. (ed.) Artificial Intelligence in Intelligent Systems. LNNS, vol. 229, pp. 450–454. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77445-5_42
Shoilekova, K.: Intelligent data analysis using a classification method for data mining knowledge discovery process. In: Silhavy, R. (ed.) Artificial Intelligence in Intelligent Systems. LNNS, vol. 229, pp. 153–157. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77445-5_14
Von Davier, M.: Statistical Models for Test Equating, Scaling, and Linking. Springer, New York (2008). https://doi.org/10.1007/978-0-387-98138-3
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This publication is developed with the support of Project BG05M2OP001-1.001-0004 UNITe, funded by the Operational Programme “Science and Education for Smart Growth”, co-funded by the European Union trough the European Structural and Investment Funds.
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Krastev, G., Voinohovska, V., Dineva, V. (2024). Enhancing Educational Assessment: Leveraging Item Response Theory’s Rasch Model. In: Silhavy, R., Silhavy, P. (eds) Data Analytics in System Engineering. CoMeSySo 2023. Lecture Notes in Networks and Systems, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-031-53552-9_17
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DOI: https://doi.org/10.1007/978-3-031-53552-9_17
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