Transforming mathematics education in Kazakhstan: evaluating the impact of innovative teaching methods on student outcomes in technical universities
Open accessAbstract
Despite Kazakhstan’s commitment to educational advancement, recent assessments have indicated opportunities to improve students’ mathematics performance. As the country continues to invest in human capital development, exploring effective pedagogical approaches to enhance mathematics proficiency at the university level has become a priority. This study investigates the impact of project-based learning (PjBL) on mathematics proficiency among university students in Kazakhstan. Using a quasi-experimental design with 341 participants across two universities and two academic cohorts, this study compares the effectiveness of PjBL with traditional lecture-based instruction. The results from the multilevel modeling analysis revealed that PjBL significantly improved students’ mathematics proficiency, with an overall effect size of d = 0.85. This study found consistent positive effects across cohorts, suggesting the robustness of PjBL as an educational intervention. These findings contribute to an understanding of effective pedagogical approaches to mathematics education at the university level in Kazakhstan, potentially informing future educational strategies aimed at enhancing students’ mathematical proficiency. This research provides empirical evidence for the efficacy of PjBL in improving mathematical skills in higher education, offering valuable insights for educational policy and practice in Kazakhstan and similar contexts.
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Research confirms that PBL significantly enhances students' problem-solving skills, knowledge transfer, and mathematical application abilities.
“At the cognitive level, research confirms that PBL significantly enhances students’ problem-solving skills, knowledge transfer, and mathematical application abilities (Saparbayeva et al., 2025; Rehman et al., 2023; Alenezi, 2023).”
From Can project-based learning improve students’ mathematics learning outcomes: a three-level meta-analysis (2026) · cites this paper as Saparbayeva et al., 2025
The Research background section distinguishes problem-based learning (PBL) from project-based learning (PjBL) and reports stronger analytical skills and retention among PBL students. Sections 2.1–2.2 and the Conclusion attribute improved mathematical problem-solving and practical application to PjBL, not PBL. The cited paper therefore does not establish the claimed PBL effects on knowledge transfer or mathematical application abilities. (LLM, conf=0.86)
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- Authors
- Saparbayeva, E., Abdualiyeva, M., Torebek, Y., Kostangeldinova, A., Tursynbayev, A., Takibayeva, G., & Sabalakhova, A.
- Affiliations
- M.Auezov South Kazakhstan State University (KZ)
- Department of Mathematics, Physics and Computer Science, NJSC ‘Shokan Ualikhanov Kokshetau University’
- Corresponding author
- Abdualiyeva, Marzhan
- Venue
- Cogent Education
- Publisher
- Informa UK Limited
- Published
- 2025-02-08
- Volume
- 12
- Issue
- 1
- ISSN
- 2331-186X (ISSN-L)
- OpenAlex
- W4407269879
- Cited by
- 11
- References
- 63
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 1
- FWCI
- 31.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cogent Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Problem and Project Based Learning
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematics Education and Programs
- Physical Sciences › Mathematics › Applied Mathematics › Mathematics Education and Pedagogy
- Keywords
- project-based learning, mathematics education, mathematics proficiency, university students, technical universities, lecture method, educational innovation, Kazakhstan, higher education, education policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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