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Naufal Ishartono
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Jl. A. Yani, Mendungan, Pabelan, Kec. Kartasura, Kabupaten Sukoharjo, Jawa Tengah 57169
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International Journal of Review in Mathematics Education
Published by Universitas Muhammadiyah Surakarta
ISSN : 31247962     EISSN : 31247962     DOI : https://doi.org/10.23917/ijrime
Core Subject :
The International Journal of Review in Mathematics Education (IJRME) is a peer-reviewed journal dedicated to advancing scholarship in mathematics education through rigorous, evidence-based review research. IJRME provides a global platform for synthesizing existing knowledge, identifying emerging trends, and addressing critical gaps in mathematics education theory and practice. The journal exclusively publishes comprehensive review studies that employ systematic, analytical, and critical methodologies to consolidate and evaluate the state of the art in the field. The journal covers all dimensions of mathematics education, such as: Curriculum design, pedagogy, and assessment Cognitive, sociocultural, and affective aspects of learning Teacher education and professional development Technological innovations (e.g., AI, digital tools) Equity, diversity, and inclusion in mathematics contexts Cross-cultural and comparative studies Policy analysis and educational reform
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Articles 5 Documents
 1 
Search results for , issue "Volume 1 No. 1: March 2026" : 5 Documents clear
Systematic Literature Review on Mathematical Representation: The Connection between Theories and Implementation Putri, Amellia; Sujadi, Imam; Nursanti, Yuli Bangun; Nurhasanah, Farida
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.12780

Abstract

Understanding and applying mathematical representation are essential in math education. However, many students face challenges in effectively using mathematical representation. Previous research has mainly focused on evaluating students' proficiency in mathematical representation rather than developing strategies to improve their skills. This has led to limited insights into research trends and information related to mathematical representation. Therefore, this SLR is crucial to fill the gap in the literature by providing a comprehensive overview of research trends on mathematical representation. This the study aims to analyze previous research related to mathematical representation, particularly the connection between the theories used, methodological features, the relationship between subjects and mathematical representation skills, as well as learning barriers and interventions to enhance these skills. At the end of the study, it was concluded that the relationship between all these variables was concluded. An examination of 52 scholarly papers on mathematical representation showed that most studies focus on assessing students' ability to use different types of mathematical representation to solve problems, drawing on theories proposed by Goldin and standards outlined by the National Council of Teachers of Mathematics (NCTM). The review also highlighted students' difficulties with mathematical representation and identified various methods for evaluating it, with written assessments and interviews being the most common approaches. Additionally, the review revealed numerous recommended activities to help students enhance their mathematical representation competency. In conclusion, the theoretical framework used in these studies significantly influences evaluation methods and recommended interventions in this field of study.
Reflective Thinking and Self-Efficacy: A Meta-Analysis and Its Implications for Mathematics Learning Santosa, Yoga Tegar; Mahmudah, Mutiara Hisda; Yulindra, Devi; Pakpahan, Akbar Waliyuddin
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.14592

Abstract

Reflective thinking and self-efficacy are two crucial constructs in education, particularly in mathematics education, and both have been reported to share a positive relationship across various contexts. However, empirical findings regarding the strength of this relationship remain inconsistent. This study presents a meta-analysis to investigate the relationship between reflective thinking and self-efficacy by addressing four research questions: (1) What is the overall strength of the relationship between reflective thinking and self-efficacy? (2) Is there significant heterogeneity among the studies? (3) Do the year of study, country, participants, educational level, and sample size serve as moderators? (4) How can the findings of this meta-analysis be integrated into mathematics learning practices? A total of 28 articles meeting the inclusion criteria were synthesized from the Scopus, ScienceDirect, and ERIC databases, following the PRISMA search protocol. The analysis involved calculating effect sizes, estimating a random-effects model, testing for heterogeneity, analyzing moderator variables, and examining publication bias. The results revealed a positive and statistically significant relationship between reflective thinking and self-efficacy (t(27) = 5.56, p < 0.001). The heterogeneity analysis indicated substantial variation among the included studies. Moderator analysis indicated that participant characteristics were a significant source of this variability, with pre-service mathematics teachers representing one of the key contributing groups. These findings highlight the importance of integrating reflective activities into mathematics learning in ways that are responsive to learner characteristics across educational levels. The results also underscore the need for future research to examine theory-driven pedagogical moderators to further clarify the mechanisms linking reflective thinking and self-efficacy.
Evolution of Deep Learning and Its Reflection on Statistical Mathematics Learning Saputra, Muhammad Akhyar Aji; Crismono, Prima; Hudi, Saman
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.14812

Abstract

This study aims to evaluate the development, topic interconnections, and global research directions in the field of Deep Learning during the period 2019–2024, while also examining its implications for teaching statistical mathematics in the digital era. A bibliometric approach was used to analyze publication trends, citation patterns, and keyword relationships with the assistance of VOSviewer software. Data were obtained from the Scopus database using the main keywords “Deep Learning,” “Neural Networks,” and “Artificial Intelligence.” The results indicate that peak research activity occurred in 2022 with a significant surge in citations, followed by a decline in 2023–2024, marking a phase of research stabilization. Network analysis revealed that topics such as computer vision, medical imaging, and unsupervised learning dominate, while emerging trends like federated learning and edge computing are beginning to develop toward privacy and computational efficiency. Geographically, the United States and China are the main contributors to scientific publications, followed by Germany, the United Kingdom, and Australia. These findings highlight that the core success of Deep Learning is fundamentally grounded in statistical mathematics, particularly in optimization and probabilistic modeling. Accordingly, the implications for teaching statistical mathematics involve reorienting curricula toward applied, data-driven contexts emphasizing probabilistic thinking, algorithmic reasoning, and the integration of computational tools. Such an approach encourages students to bridge theoretical understanding with real-world problem solving in artificial intelligence and data science.
Social Constructivism: Principles and Implications to Mathematics Learning Mangwende, Edmore
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.15221

Abstract

This paper examines social constructivism as a learning theory and its implications in mathematics learning. The study followed a conceptual qualitative paradigm. The researcher used secondary data obtained from journal articles, e-books, periodicals and websites. Social constructivism emphasizes active construction of knowledge by learners through interaction with others and the environment. According to the theory, the learner understands the world through experiencing it. Reality is socially constructed and it depends on individual interpretation. In mathematics learning, the theory implies that mathematics tasks make sense to the learners if they solve real life problems. Mathematics learners should not simply memorise concepts, but should critically analyse their own and other people’s mathematical perspectives. More marks should be awarded for the thought process than for the final solution to a mathematical task.   Mathematics learning should be taken beyond the classroom. Formal mathematics learning can be built atop the knowledge gained by the learners through culturally performed tasks. The mathematics learners should view their teacher as a facilitator, co-learner, co-explorer and co-constructor of knowledge. Teaching strategies that promote collaboration and active participation of learners are desirable. Peer and self assessment help to foster active and interactive mathematics learning.
Adaptive Learning and Generative AI in Mathematics Teacher Education: A Systematic Review Maryati, Iyam; Harun, Makmur; Gumilar, Surya; Rahayu, Ayu Puji
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.15936

Abstract

This study presents a systematic review of the current literature on the integration of adaptive learning and generative AI (GenAI) in developing technological pedagogical content knowledge (TPACK) and statistical literacy of prospective mathematics teachers. By analyzing 76 selected articles published between January 2023 and May 2025, this study uses the PRISMA framework and thematic synthesis with the help of NVivo 12 Plus software. The results of the study indicate that adaptive learning environments supported by GenAI can strengthen learning personalization, provide responsive feedback, and visualize content, which significantly contribute to the development of TPACK, especially in the TPK and TCK domains. In addition, GenAI supports the strengthening of statistical literacy through data-driven instructional tools that foster the ability to interpret, represent, and understand the context of data. This study successfully formulated a conceptual framework for adaptive learning integrated with GenAI that reflects the pedagogical needs and specific content in mathematics education. These findings have important implications for teacher education programs, especially in facilitating professional competencies that are adaptive to technological advances and the demands of ethical and reflective data-driven learning.

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