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All Journal Kognitif: Jurnal Riset HOTS Pendidikan Matematika International Journal of Ethno-Sciences and Education Research Journal on Mathematics Education
Rais, Damar
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Religion Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Transportation Other

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Elevating student engagement and academic performance: A quantitative analysis of Python programming integration in the Merdeka Belajar curriculum Rais, Damar; Zhao, Xuezhi
Journal on Mathematics Education Vol. 15 No. 2 (2024): Journal on Mathematics Education
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jme.v15i2.pp495-516

Abstract

Python programming is widely employed in educational institutions worldwide. Within the Merdeka Belajar curriculum context, this programming is recognized as a suitable vehicle for mathematics instruction, significantly influencing students’ motivation and learning outcomes, particularly following periods of educational hiatus. This study examines the effectiveness of Python programming in promoting heightened learning outcomes by examining the intricate relationship between student motivation and learning. The study uses quantitative research methodologies to evaluate student learning facilitated through Python programming, encompassing problem-solving assessments and the administration of motivation questionnaires. By engaging in coding practices, students understand the symbols they manipulate, facilitating their ability to juxtapose data derived from mathematical modeling with the resultant programming output. When disparities arise, students are empowered to reassess their work, fostering a more profound comprehension of the subject matter. These exercises serve to augment students' capacity to retain and process information within memory. Furthermore, students demonstrate a favorable disposition, exhibiting persistence in resolving programming challenges by meticulously analyzing error outputs, particularly those pertaining to TypeErrors. Encouraging students to confront errors through thoroughly examining error output manifestations engenders an efficacious learning paradigm. This research proffers invaluable insights for educational institutions contemplating the integration of Python programming as an instructional adjunct.
Comparison of Teaching Content on Inequalities between China and Indonesia Xuezhi , Zhao; Rais, Damar
Kognitif: Jurnal Riset HOTS Pendidikan Matematika Vol. 5 No. 4 (2025): October - December 2025
Publisher : Education and Talent Development Center Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/kognitif.v5i4.4253

Abstract

In this study, we select inequality content from a set of Indonesian instructional materials and compare it with the corresponding inequality content in the People’s Education Press (PEP) textbooks. The comparison is significant because the two education systems represent distinct pedagogical approaches that differ historically, culturally, and epistemologically. Moreover, they are grounded in contrasting educational philosophies. PEP follows a Chinese tradition emphasizing formal structure, systematic practice, and deductive reasoning that moves from worked examples to general rules. In contrast, the Indonesian Kurikulum Merdeka is rooted in Freudenthal’s Realistic Mathematics Education (RME), which encourages contextual modeling and meaning-making prior to the introduction of formal symbols. By comparing the topic of inequalities—an abstract concept with specific operational rules (such as reversing the inequality sign when multiplied by a negative number)—we can observe how these approaches lead to different learning experiences. Do students discover rules through structured exploration, or are they expected to apply rules within real-world contexts after the concepts are presumed to be understood? In Indonesia, inequalities are taught primarily in Grade 11 of senior high school, under the assumption that students already possess a mature level of abstract reasoning. In China, however, inequality-related content is distributed across multiple stages of elementary and secondary schooling. Both countries have undergone substantial curriculum reforms aimed at improving mathematical proficiency and educational equity, yet their textbooks continue to reflect distinct pedagogical philosophies and cultural priorities. By examining the differences across these dimensions, we hope to provide useful insights for the teaching of inequality-related content
From Concept Image to Computational Thinking: A Design-Based Research on Python-Integrated Mathematics Learning Rais, Damar
International Journal of Ethno-Sciences and Education Research Vol. 6 No. 1 (2026): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v6i1.1174

Abstract

The increasing demand for computational thinking (CT) in mathematics education requires instructional designs that meaningfully connect abstract mathematical concepts with algorithmic reasoning. Grounded in the theory of concept image, this study investigates how Python-integrated learning activities can foster students’ mathematical understanding while simultaneously supporting the development of computational thinking. Using a Design-Based Research (DBR) methodology, this study was conducted across three iterative cycles involving undergraduate mathematics students and how Python-integrated learning activities can enrich students' mathematical understanding while simultaneously developing CT skills. The study was conducted across three iterative cycles in a Calculus II, Integration course involving twenty-six mathematics education students PGRI University of Yogyakarta. Data were collected through concept image mapping, CT performance assessments, classroom observations, and the analysis of Python code artifacts. The finding indicate that Python-assisted dynamic visualization facilitated a transition from static, procedural understanding toward deep, relational mental representations. Programming activities were proven to strengthen abstraction and algorithmic reasoning capabilities, where code serves as an externalization of students' concept images. This study yields three key instructional design principles: Concept-First Coding, Representational Fluidity, and Reflective Alignment. In conclusion, Python integration designed as a "cognitive bridge" effectively transforms mathematical intuition into formal-computational understanding that is transferable to complex problem-solving contexts. By leveraging programming as a representational medium, educators can create rich, interactive learning ecosystems where students actively construct knowledge, refine mental models, and develop transferable cognitive competencies. Future directions may include expanding these practices across disciplines, refining assessment models for CT in text-based programming, and investigating long-term retention and applicability of learned skills beyond academic settings.
Co-Authors Xuezhi , Zhao Zhao, Xuezhi