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Naufal Ishartono
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ijrime@ums.ac.id
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ni160@ums.ac.id
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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
Arjuna Subject : -
Articles 13 Documents
 1   2 
ARIMA Forecasting of Ordinary Level Mathematics Pass Rates: A 13-Year Critical Review from a Zimbabwean High School Edward Nsingo; Silvanos Chirume
International Journal of Review in Mathematics Education Volume 1 No. 3: September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.15830

Abstract

This study applies time series analysis and ARIMA forecasting to the Ordinary Level Mathematics results of one high school in Bulawayo, Zimbabwe, for the period 2010 to 2022—a period following the 2008–2009 emigration of qualified teachers that was anticipated to disrupt mathematics performance. Secondary results data were smoothed and analyzed in R Studio to identify the underlying trend through regression analysis, and an ARIMA model was fitted to forecast the school's future pass rate. To explain the observed pattern face-to-face interviews were conducted with all ten qualified mathematics teachers at the school. The pass rate followed a repeating cycle of a fall in one year being usually followed by an improvement in the next two years and both the fitted ARIMA (1,1,1) model and trend line suggested a small improvement by the sixteenth year of the series, 2025. Interview evidence linked this cycle chiefly to the rotation of teachers between examination and non-examination classes. The study recommends that the school deploy teachers with a demonstrated record of improving pass rates to examination classes, allocate more revision time, and introduce performance-based incentives while extending this line of research to the district and provincial level.
A Systematic Critical Review: Flipped Learning and Its Impact on Self-Efficacy in Mathematics Education Naufal Ishartono; Rizky Oktaviana Eko Putri; Kristof Fenyvesi; Manuel O. Malonisio
International Journal of Review in Mathematics Education Volume 1 No. 3: September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.17530

Abstract

Although the flipped learning model has been widely recognized to improve student engagement, its specific pedagogical effectiveness on self-efficacy in the context of abstraction-laden mathematics education has not been explored in depth. In addition, there has been no critical study that dissects the ambiguity of empirical findings related to operational challenges and cognitive burdens faced by students and educators in the field. Therefore, this critical review research aims to investigate the impact of the implementation of flipped learning on mathematical self-efficacy while identifying methodological and operational challenges in practice. The study adopted the PRISMA framework and the PICO search strategy to curate and critically evaluate 10 highly reputable empirical articles published in the 2020–2025 time frame from the Scopus, Web of Science, and ERIC databases. The results of the synthesis showed that flipped learning was significantly able to significantly improve math self-efficacy, mitigate anxiety, and serve as an equity intervention for low-performing students, especially when supported by self-regulated cognitive strategies (SRCS) and interactive media in the pre-grade phase. However, this success was severely limited by significant challenges, including students' difficulties in understanding abstract concepts independently, spikes in workload, as well as the scarcity of digital literacy and time constraints experienced by educators. Theoretically and practically, these findings provide an essential foundation for educators and policymakers to integrate pedagogical scaffolding and assistive technologies, such as AI-driven chatbots, to minimize students' cognitive barriers and design a more responsive and equitable future math learning ecosystem.
Deep Learning Research Trends in Mathematics Learning: A Scoping Review And Bibliometric Analysis (ScoRBA) Tazkia Amalia; Hodiyanto Hodiyanto
International Journal of Review in Mathematics Education Volume 1 No. 3: September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.18504

Abstract

This study aims to map the research landscape of deep learning in mathematics education using bibliometric mapping based on Scopus indexed data from 2011 to 2025. Using scientific mapping methods via VOSviewer and R Bibliometrix, a total of 201 publitions meeting the inclusion criteria were analyzed. To address construct validity, the study explicitly differentiates between deep learning as a computational artificial intelligence (AI) methodology and deep learning as a pedagogical concept (deep vs. surface learning), analyzing their respective representations across clusters. The findings demonstrate: (1) a rapid growth trajectory in publications, peaking at 45 articles in 2025; (2) the top contributing journals are Education Sciences, Eurasia Journal of Mathematics, Science and Technology Education, Frontiers in Psychology, International Journal of Mathematical Education in Science and Technology (5 articles each), and Educational Studies in Mathematics (4 articles); (3) Y.F. Zakariya is the most prolific author (4 articles); (4) Beijing Normal University (China) and Universitetet i Agder (Norway) are the most productive institutional affiliations; (5) the United States leads global production (61 articles), followed by China (28 articles) and Australia (16 articles), with Indonesia ranking fourth (11 articles); and (6) three primary thematic clusters were delineated: the Red Cluster (AI computational models and e learning applications), the Green Cluster (pedagogical constructs of mathematical deep learning and cognition), and the Blue Cluster (STEM/STEAM integration and instructional design). Emerging frontiers highlight the integration of generative AI at the elementary education level and cross disciplinary STEAM frameworks, offering promising avenues for future research.

Page 2 of 2 | Total Record : 13

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