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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Pendidikan IPA Indonesia
Zhanatbekova, Nazym
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Education

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Digital learning and student outcomes: a mathematical synthesis from the last decade Koishybekova, Aizhan; Zhanatbekova, Nazym; Khaimuldanov, Yerlan; Orazbayeva, Assel; Abykenova, Dariya
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i2.30431

Abstract

Understanding the broad effects of e-learning on educational outcomes and the contributing factors is crucial, especially given the conflicting conclusions from past research. This is important to ensure that educators and policymakers do not waste resources and focus effectively when prioritizing digital investments. Hence, this study sought to provide a comprehensive quantitative review of the extant evidence on how digital learning initiatives affect student outcomes within the cognitive domain across different subjects and educational levels. To that end, a meta-analysis was performed encompassing 17 studies spanning from 2015 to 2023, involving 1,896 participants. The quantitative synthesis was completed using a random-effects model. The results indicate a positive small to medium overall effect size (Hedge’s g=.49, adjusted for publication bias) for technology-assisted interventions compared to traditional education. Subgroup analyses revealed nuances, such as higher academic gains associated with active cognitive engagement modes and potential disparities between school and higher education settings. However, no factors significantly affected the pooled effect sizes for cognitive outcomes. Nevertheless, considerable between-study heterogeneity could compromise the estimates. The meta-analysis underscores the scarcity of rigorous studies in the digital learning domain. Further research directions are outlined.
The Role of Artificial Intelligence (AI) in Personalised Physics Education Abdulayeva, Aigerim; Zhanatbekova, Nazym; Andasbayev, Yerlan; Khaimuldanov, Yerlan; Zhiyembayev, Zhomart
Jurnal Pendidikan IPA Indonesia Vol. 14 No. 3 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v14i3.29752

Abstract

This study aimed to establish conceptual mechanisms and patterns for applying artificial intelligence (AI) technologies to personalised physics education within the Kazakhstani educational system. Methods. A comprehensive methodology was employed, combining both theoretical and empirical approaches. The theoretical phase involved reviewing regulatory documents in Kazakhstan’s education system. The empirical phase consisted of a pedagogical experiment conducted between September and December 2024 across three educational institutions in Taldykorgan, Kazakhstan. The study involved 58 tenth-grade students, divided into experimental and control groups; the experimental group utilised an AI-driven personalised learning system designed to adapt content based on student performance. Data were collected on academic performance, theoretical knowledge, practical skills, and research competencies using pre-tests, interim assessments, and final evaluations. Results. The experimental groups demonstrated a significant improvement in academic performance, with the average score increasing from 4.2 to 4.6. 76% of students in experimental groups successfully solved advanced problems, compared to 52% in control groups. The system fostered improved critical thinking, research competencies, and self-assessment skills, while enhancing students’ ability to engage in scientific discourse and apply knowledge in interdisciplinary contexts. Conclusions. The AI-driven model proved highly effective at personalising learning and improving students' academic and cognitive outcomes. It offers a scalable framework for adapting content to individual learning styles, with positive impacts on motivation and problem-solving abilities. The findings contribute to a growing body of research on AI applications in education and provide a foundation for further advancements in personal learning systems.
Co-Authors Abdulayeva, Aigerim Abykenova, Dariya Andasbayev, Yerlan Khaimuldanov, Yerlan Koishybekova, Aizhan Orazbayeva, Assel Zhiyembayev, Zhomart