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Abdul Halim Fathani
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Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang MT. Haryono 193 Malang, East Java, Indonesia
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Jurnal Pendidikan Matematika (JPM)
Published by Universitas Islam Malang
ISSN : 24424668     EISSN : 26564564     DOI : https://doi.org/10.33474/jpm.v12i1.24984
Core Subject : Education, Social,
Education Mathematics Social Sciences
The Jurnal Pendidikan Matematika (JPM) welcomes contributions that encourage the exchange of knowledge, rational discussion, innovative ideas and techniques that have the potential for significant impact, between researchers/academicians and practitioners. JPM covers a broad range of areas, including but not limited to: model development, media development, and the creation of mathematics teaching materials. The journal also explores pedagogical content knowledge, the evaluation of mathematics learning, and the processes involved in mathematical thinking. Additionally, it examines both the theoretical and practical aspects of mathematics education and studies related to the integration of mathematics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 326 Documents
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Mathematical conceptual understanding under geogebra-assisted MASTER learning: The role of students’ self-efficacy Lia Fauziah; Bambang Sri Anggoro; Abi Fadila
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25859

Abstract

This study examines differences in students’ mathematical conceptual understanding across instructional conditions, the role of self-efficacy in mathematical conceptual understanding, and the interaction between the GeoGebra assisted MASTER learning model and self-efficacy. The study used a quantitative, quasi-experimental design. The study population is all grade VIII students of Public Junior High School (SMP Negeri) 1 Bukit Kemuning. The sample comprised two experimental classes and one control class selected through cluster random sampling. Data were obtained using a mathematical conceptual understanding test and a self-efficacy questionnaire. The mathematical topic was circles. The intervention was conducted for five sessions in each class, with each session lasting 2 × 45 minutes. The data were first examined for normality and homogeneity before testing the hypotheses using two-way ANOVA in a 3 × 3 factorial design. The findings showed significant differences in students’ mathematical conceptual understanding across instructional conditions (p < .001). Self-efficacy was also significantly related to students’ mathematical conceptual understanding (p < .001). However, the interaction between the learning model and self-efficacy was not statistically significant (p = .170). Thus, the GeoGebra-assisted MASTER learning model may serve as an alternative instructional approach to support students’ mathematical conceptual understanding.
Students’ mathematical critical thinking during project-based probability learning: A repeated-measures study Febi Widarwati; Dian Devita Yohanie; Darsono
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25861

Abstract

In the 21st century, mathematical critical thinking is a core competency that students must master. However, high school students still struggle to analyze word problems in mathematics. This study examines how students’ mathematical critical thinking skills develop over three Project-Based Learning (PjBL) sessions on probability. The study employs a quantitative approach with a pre-experimental, single-group repeated measures (time-series) design, involving 35 10th-grade students at SMAN 3 Kediri (Public Senior High School 3 Kediri). Data were collected through essay-based word-problem tests at the end of each session and assessed using a rubric based on the six FRISCO indicators (Focus, Reasoning, Inference, Situation, Clarity, and Overview), adapted from Ennis. The data were analyzed using the nonparametric Friedman and Wilcoxon Signed-Rank tests because the distributions of Test 2 and Test 3 scores deviated from normality. Average scores increased gradually from 59.86 (Test 1) to 61.61 (Test 2) and 69.04 (Test 3), with a statistically significant increase only observed from Test 2 to Test 3. At the indicator level, “Focus” and “Situation” increased sharply in the third session; “Reason” remained the strongest indicator, while “Inference” and “Overview” remained the weakest. These findings suggest that students’ mathematical critical thinking skills developed gradually throughout the PjBL sequence, although the single-group design without a control group limits causal claims regarding PjBL’s contribution. Teachers are advised to supplement PjBL with explicit scaffolding during the inference and answer evaluation stages.
Exploring senior high school students' perceptions of artificial intelligence as a mathematics learning assistant Ihqlil Sekti Nugroho; Dian Devita Yohanie; Darsono Darsono
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25877

Abstract

The development of Artificial Intelligence (AI) has presented various opportunities in education, including as a learning assistant in mathematics. This study aims to determine students' perceptions of the use of AI as a learning assistant, identify the forms of AI utilization in mathematics learning, and assess the risks and limitations of its use. This study employed a descriptive qualitative approach. Questionnaire responses from 31 students were used to provide an initial description of students' perceptions. In comparison, semi-structured interviews with five selected students were conducted to obtain in-depth qualitative data at Public Senior High School (SMA Negeri) 7 Kediri. Data were collected through questionnaires and semi-structured interviews, then analyzed using thematic analysis. The results showed that students had a positive perception of the use of AI in mathematics learning. AI is used primarily to help understand material, obtain additional explanations, and solve math problems. However, students still rely more on teachers and other learning resources to understand mathematical concepts in depth. This study also found that students are aware of the risks of using AI, such as potential dependency, reduced self-study efforts, and decreased independent thinking skills if used excessively. Furthermore, nearly all students believed that AI cannot replace the role of teachers in learning. These findings indicate that students perceive AI as a potentially useful learning assistant for mathematics learning when its use is guided by teachers and accompanied by critical evaluation of AI-generated information.
Photomath-assisted contextual learning and students’ mathematical reasoning: examining the role of logical-mathematical intelligence Ela Maya Firanti; Darmina Eka Sari Rangkuti
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25881

Abstract

This study aims to examine the effect of Photomath-assisted contextual learning on students’ mathematical reasoning and to investigate the moderating role of logical-mathematical intelligence in the relationships between learning and mathematical reasoning at Islamic Junior High School Madrasah Tsanawiyah (MTs) Al Washliyah Tanjung Morawa. This study employs a quantitative approach with a quasi-experimental pretest-posttest control group design. The study population consisted of 129 eighth-grade students. Using purposive sampling, two classes were selected: 31 students in the experimental class and 32 students in the control class. The research instruments consisted of a mathematical reasoning test and a logical-mathematical intelligence questionnaire. The data were analyzed using an independent-samples t-test and two-way ANOVA . The data were analyzed using an independent-samples t-test, which showed no statistically significant difference in logical-mathematical intelligence scores between the experimental and control groups, t(61) = 1.662,  . The two-way ANOVA revealed a statistically significant main effect of the learning class on students’ mathematical reasoning, F(1,59) = 83.551, p < 0.001. In contrast, the main effect of logical-mathematical intelligence was not statistically significant, F(1,59) = 0.215, p = 0.644. The interaction between learning style and logical-mathematical intelligence was also not statistically significant, F (1,59) = 0.456, p = 0.502. Thus, logical-mathematical intelligence did not have a statistically significant moderating effect on the relationships between learning style and students’ mathematical reasoning.
Mathematical comparison of risk-score transformations for dynamic risk trajectories in mathematics learning Nindy Aulia Eka Puspita; Syahrul Tri Armanda
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25918

Abstract

Learning Analytics has become an important approach for supporting mathematics learning evaluation through Early Warning Systems (EWS). However, most existing studies directly employ prediction probabilities generated by machine learning models without examining how different mathematical probability transformations influence the representation of students' risk evolution over time. This study presents a mathematical analysis of probability transformation functions within a Dynamic Learning Analytics framework using the Open University Learning Analytics Dataset (OULAD). Student risk scores were estimated using LightGBM at five Learning Progress Checkpoints (LPC20–LPC100) and subsequently transformed using five nonlinear probability functions, namely Sigmoid, Tanh, Probit, Arctan, and Softsign. The transformed trajectories were quantitatively evaluated using variance, mean slope, total variation, and risk accumulation, while Dynamic Time Warping and Agglomerative Clustering were employed to identify temporal risk patterns. The results demonstrate that different transformation functions produce distinct mathematical characteristics despite preserving similar cumulative risk distributions. Under the selected variance, mean absolute slope, and total variation criteria, Softsign produced the least variable and smoothest transformed trajectories, with the lowest variance (0.1048) and total variation (0.3320).  Based upon the types of trajectories identified, there were four representative types of trajectories identified: Persistence, Escalation, Recovery, and Instability. These results provide a descriptive mathematical comparison of the five risk-score transformations and show that the choice of transformation affects the stability and temporal characteristics of trajectory-based representations.
Students’ mathematical conceptual understanding following deep learning supported by wordwall: a one-group pretest–posttest study Tiya Anjelita; Mu’jizatin Fadiana
Jurnal Pendidikan Matematika (JPM) Vol 12 No 2 (2026): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v12i2.25976

Abstract

Students need to acquire mathematical conceptual understanding to be able to explain, relate, and apply mathematical ideas in different problem situations. However, the learning of mathematics at public Junior High School (SMP Negeri) 1 Kenduruan is still limited to the use of interactive digital media. Previous studies on the integration of deep learning supported by Wordwall to improve mathematical conceptual understanding have been limited. The aim of this study was to analyze the changes in the mathematical conceptual understanding of eighth-grade students after being given deep learning supported by Wordwall. A quantitative pre-experimental, one-group pretest-posttest design was used. The participants were 31 students of Class VIII-E selected using purposive sampling. The intervention was delivered in five sessions. The intervention was delivered in five sessions on cubes and rectangular prisms and consisted of one pretest, three deep learning sessions with Wordwall activities (Group Sort, Spin the Wheel, and Quiz), and one posttest. Data were collected using a mathematical conceptual understanding test and analyzed using descriptive statistics, the Shapiro-Wilk test, paired-samples t-test, and N-Gain analysis. The average score increased from 33.629 on the pretest to 74.677 on the posttest. A paired-samples t-test showed that there was a statistically significant difference, t(30) = −21.282, p < .001. The average N-Gain score was 0.625, which was categorized as moderate. These findings indicate enhanced conceptual understanding of mathematics following the intervention. But a one-group design without a control group limits causal interpretation. Larger samples, control groups, and broader learning contexts should be included in future studies.

Page 33 of 33 | Total Record : 326

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