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Abstract
This study aims to examine the levels of mathematical literacy and assessment literacy among prospective mathematics teachers in Kazakhstan, reveal the relationship between these two constructs, and identify profile differences among prospective teachers. A quantitative research design was used in the study, and data were collected from 335 teacher candidates (196 female, 135 male) from seven universities via an online survey. A scale developed based on a literature review and the PISA framework was used for mathematical literacy, while the Assessment Literacy Standards Version 6.0 scale was adapted into Kazakh for assessment literacy. A Bayesian statistical approach was adopted for data analysis, Kendall tau-b correlations were calculated, and latent profile analysis (LPA) was applied. The findings showed that teacher candidates had high perceived competence levels in all seven dimensions of both mathematical literacy and assessment literacy. Overall equality was observed in terms of the gender variable. Moderately strong positive relationships were found between all dimensions of mathematical literacy and assessment literacy. Math anxiety, however, did not show any meaningful relationship with any construct. Latent profile analysis identified three distinct groups: Assessment Experts, Assessment Practitioners, and Assessment Novices. While there were significant differences between the groups in terms of mathematical literacy, math anxiety remained similar. The study offers empirical evidence on the correlation between mathematical literacy and assessment literacy, while also uncovering the diverse composition among teacher candidates. The results show that teacher education programs need to include these two domains of literacy in a way that is connected and provide multiple ways to educate based on individual profiles.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 21, Issue 12, December 2025, Article No: em2748
https://doi.org/10.29333/ejmste/17494
Publication date: 03 Dec 2025
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