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Yohanis Ndapa Deda
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Range : Jurnal Pendidikan Matematika
Published by Universitas Timor
ISSN : 26852373     EISSN : 26852373     DOI : https://doi.org/10.32938/jpm
Core Subject : Education,
Education Mathematics
Range: Jurnal Pendidikan Matematika publishes original research or theoretical papers about teaching and learning in mathematics education study program on current science issues, namely: (1) Mathematics educator in elementary, secondary and high school level, (2) Mathematics observers and researchers, (3) Educational decisions maker on regional and national level. We recommend classroom action research, qualitative, descriptive, or quantitative. We may process manuscript of didactic development research (DDR) or research and development (R & D). Our publication could be research on teaching method, experiment of teaching aid or media, and even effectiveness of lesson study. We accepted any manuscript derived from research of mathematics education. We will not process manuscript from research approach of school management.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 153 Documents
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Digital Game-Based Learning in Primary Mathematics: A Systematic Review and Meta-Analysis of Research Trends and Learning Outcomes Yulianto, Dwi; Situmeang, Muhamad Mukri; Astari; Nurcahyo, Rahmat
RANGE: Jurnal Pendidikan Matematika Vol. 7 No. 2 (2026): Range Januari 2026
Publisher : Pendidikan Matematika UNIMOR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jpm.v7i2.10225

Abstract

This study presents a systematic literature review and meta-analysis of 37 empirical articles examining the implementation of Digital Game-Based Learning (DGBL) in primary school mathematics education from 2018 to 2025. Utilizing the PRISMA framework and thematic coding techniques, the study analyzes publication trends, geographical distribution, mathematical content coverage, research methodologies, as well as the types and modes of games employed. The findings indicate a significant increase in publications after 2020, with 42% originating from Asia, particularly China, Indonesia, and Malaysia. Numeracy topics, such as numbers and arithmetic operations, dominate 86% of the studies, while topics like statistics and probability remain underrepresented. Quantitative approaches, especially experimental designs, account for 68% of the methodologies used. Mini-games and individual gameplay modes are the most commonly adopted, whereas collaborative and narrative-based modes remain limited. The meta-analysis reveals that DGBL has a significant positive impact on conceptual understanding, learning motivation, and problem-solving skills. These findings highlight the importance of developing more inclusive, contextualized, and adaptive DGBL designs to strengthen foundational mathematical literacy in the digital era.
Student’s Mathematical Problem Solving Skills and Self-Efficacy on Word Problems through Technology-Based Learning Arif Saefuloh, Nandang; Gilar Jatisunda, Mohamad; Syofiana, Mardiyah; Mulyanti, Yanti
RANGE: Jurnal Pendidikan Matematika Vol. 7 No. 2 (2026): Range Januari 2026
Publisher : Pendidikan Matematika UNIMOR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jpm.v7i2.10322

Abstract

Students’ ability to solve mathematical word problems is closely linked to their mathematical problem-solving skills and self-efficacy. This study examines the influence of two technology-based learning approaches—synchronous learning (via Zoom and Google Meet) and asynchronous learning (via WhatsApp)—on students' problem-solving performance and self-efficacy levels. The research involved 27 seventh-grade students in Bandung and employed a mixed-method design with an explanatory sequential approach. The findings revealed a strong relationship between the type of technology-mediated learning and students’ self-efficacy in mathematical problem solving. Students who engaged in synchronous learning demonstrated better performance in interpreting problems, developing strategies, and justifying solutions compared to those who used asynchronous methods. Learners with moderate to high self- efficacy consistently outperformed those with low self-efficacy, particularly in identifying relevant information, making conjectures, and generalizing patterns. In contrast, students with low self-efficacy showed difficulties in solving word problems and exhibited limited use of key processes in problem solving. These results emphasize the importance of aligning instructional approaches with students' self-efficacy levels and providing structured support for students involved in asynchronous learning to strengthen their mathematical problem-solving abilities.
Estimation of Path Coefficient Parameter Based on The Best RMSEA Value in Structural Equation Modeling Weighted Least Square Simarmata, Justin Eduardo; Mone, Ferdinandus; Chrisinta , Debora; Purnomo, Miko; Matute, Alejandro Jr. V.
RANGE: Jurnal Pendidikan Matematika Vol. 7 No. 2 (2026): Range Januari 2026
Publisher : Pendidikan Matematika UNIMOR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jpm.v7i2.10324

Abstract

Structural Equation Modeling (SEM) is a statistical approach widely used to analyze causal relationships between latent and observed variables. A key issue in SEM lies in selecting an appropriate parameter estimation method, as it strongly affects the accuracy and interpretation of results. Among the most common estimation techniques are Maximum Likelihood (ML) and Weighted Least Squares (WLS). This study aims to compare the performance of ML and WLS in estimating path coefficients within SEM analysis. Using simulated data generated with the simulateData() function from a predefined structural model, three scenarios are examined with sample sizes of 500 and 1000. Data transformation procedures are applied to ensure consistency before model testing. Each SEM model is then estimated using both ML and WLS, and the results are evaluated through Root Mean Square Error of Approximation (RMSEA) values obtained from 100 replications. Findings indicate that WLS generally outperforms ML in terms of model fit and stability. In the first scenario with a sample size of 500, WLS achieves a lower average RMSEA (0.0141) compared to ML (0.0172). With a sample size of 1000 in the second scenario, both methods produce similar RMSEA values (0.009 for WLS and 0.0096 for ML), though WLS demonstrates lower variability. In the third scenario, also with a sample size of 1000, WLS records an average RMSEA of 0.0074 versus 0.0092 for ML. Overall, the results suggest that WLS is more effective and reliable than ML in providing accurate parameter estimates across different data conditions and sample sizes.

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