Jurnal Penalaran dan Riset Matematika

CRITICAL NUMERACY BASED ON MAJAPAHIT ETHNOMATHEMATICS: SYNTHESIZING CRITICAL THINKING AND NUMERACY LITERACY

2026-10-24T12:34:08+00:00

Critical thinking and numeracy literacy have largely been studied as two standalone competencies, so their integration in mathematics learning remains limited. This conceptual article aims to synthesize critical thinking and numeracy literacy into the construct of critical numeracy, and to position digital ethnomathematics as a contextual vehicle for its development. The study employed a narrative review method of literature published between 2015 and 2026, retrieved through the Scopus, Web of Science, and Google Scholar databases. The synthesis shows that critical numeracy is a capacity that integrates the dimensions of critical thinking (FRISCO) with numeracy literacy through four key capabilities Understanding, Criticality, Community, and Creativity which are explicitly distinguished from earlier versions of the critical numeracy construct through their mapping onto the FRISCO framework. This construct aligns with the principles of Deep Learning (Meaningful, Mindful, and Joyful Learning) within the Merdeka Curriculum, while digital ethnomathematics grounded in Majapahit culture is proposed as an authentic context that connects mathematical reasoning with real-life problems. This article produces a Conceptual Model of Critical Numeracy Based on Majapahit Ethnomathematics as a theoretical foundation for developing assessment instruments, instructional models, and empirical research at the junior high school level in Indonesia.

STUDENTS’ LEARNING STYLES AND THEIR IMPLICATIONS FOR MATHEMATICS LEARNING

2026-10-24T16:31:59+00:00

This study aimed to describe the learning style tendencies of Grade IV students at SDK Ursula Ende and analyze their implications for mathematics learning. Each individual has unique preferences for receiving and processing information, known as learning styles. Understanding students’ learning styles provides an important basis for designing effective mathematics learning strategies. This study used a qualitative descriptive approach involving direct observation, a learning-style questionnaire based on the VAK (Visual, Auditory, Kinesthetic) model, and structured interviews. The participants were 30 Grade IV students studying fractions, the least common multiple (LCM), and the greatest common divisor (GCD) in mathematics class taught by Ms. Maria Erlina. The results showed that 46.4% of students had a visual learning style, 32.1% a kinesthetic style, and 21.4% an auditory style. The observation findings revealed that some students had difficulty finding common denominators for fractions and distinguishing between the LCM and GCD, which was closely associated with the predominance of a visual learning style that had not been fully accommodated. The study indicates that teachers need to implement differentiated learning strategies by integrating visual media, concrete teaching aids, and verbal discussion. Aligning teaching methods with students’ learning styles was found to increase active engagement and optimize understanding of mathematical concepts.

ALGORITHMIC BIAS AND ETHNOMATHEMATICS IN AI-BASED MATHEMATICS EDUCATION: A CRITICAL REVIEW AND INCLUSIVE PEDAGOGY

2026-10-30T08:13:46+00:00

The use of artificial intelligence (AI) in mathematics education expands opportunities for adaptive feedback and learning support but also raises concerns regarding algorithmic fairness, access, cultural representation, and student agency. This article aims to explain how these opportunities and risks are interconnected and to develop a pedagogical framework integrating ethnomathematics, critical mathematics education, and equitable AI governance. The study employs a critical integrative review of methodological, theoretical, empirical, and policy literature whose publication details can be verified. The literature is interpreted through four analytical dimensions: the learning benefits of AI, sources of inequity, recognition of culturally grounded mathematical knowledge, and conditions for implementation and governance. The synthesis indicates that the effects of AI cannot be generalized as invariably harmful or invariably beneficial. Inequities may arise when data, assessment design, device access, and pedagogical decisions disregard learners' contexts. Conversely, technology can support conceptual exploration when teachers retain instructional control and provide meaningful tasks. The article develops the Critical Techno-Ethnomathematics Pedagogical Framework (KPT-EK), comprising four pillars: algorithmic fairness auditing, dialogic cultural modeling, critical socioeconomic numeracy, and digital agency. The framework is operationalized through five instructional phases, multilevel evaluation indicators, and propositions for empirical testing. The article contributes a more balanced argument about AI and a testable pedagogical design. Its findings do not yet constitute evidence of the framework's effectiveness or a completed scoping review with an audited corpus of studies.