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All Journal Jurnal Infinity APOTEMA : Jurnal Program Studi Pendidikan Matematika Jurnal Kesehatan Jurnal Riset Pendidikan dan Inovasi Pembelajaran Matematika (JRPIPM) Jurnal Peduli Masyarakat Jurnal ABDIKU Journal of Research in Science and Mathematics Education Jurnal Infinity
enny listiawati, enny
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Religion Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Understanding mathematics prospective teachers' comprehension of function derivatives based on APOS theory: Insights from low mathematics anxiety levels Listiawati, Enny; Juniati, Dwi; Ekawati, Rooselyna
Jurnal Infinity Vol 14 No 2 (2025): VOLUME 14, NUMBER 2, INFINITY
Publisher : IKIP Siliwangi and I-MES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22460/infinity.v14i2.p483-512

Abstract

Understanding function derivatives shows global patterns of difficulty in comprehension and application. More research is needed to examine students' understanding of APOS theory. This research analyzes prospective mathematics teacher students' understanding of function derivatives based on mathematics anxiety. This study used a qualitative-exploratory design to describe the understanding of function derivatives of prospective mathematics teacher students with APOS theory, considering mathematics anxiety through assignments and interviews. A saturated sample of 26 students was studied. Instruments included math anxiety questionnaires, math ability tests, and function derivative tasks. Data was analyzed using triangulation, peer debriefing, member checking, data reduction, presentation, conclusion, and verification. The study of function derivatives, based on APOS Theory, integrates mental structures and mechanisms like encapsulation and coordination, showing proficiency in simple function derivatives and composition function derivatives but challenges with graphing function derivatives. This research emphasizes the need for teaching strategies that address math anxiety to improve conceptual understanding. It encourages further study of teaching interventions, emotional support, and the long-term impact of math anxiety.
Understanding mathematics prospective teachers' comprehension of function derivatives based on APOS theory: Insights from low mathematics anxiety levels Listiawati, Enny; Juniati, Dwi; Ekawati, Rooselyna
Jurnal Infinity Vol 14 No 2 (2025): VOLUME 14, NUMBER 2, INFINITY
Publisher : IKIP Siliwangi and I-MES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22460/infinity.v14i2.p483-512

Abstract

Understanding function derivatives shows global patterns of difficulty in comprehension and application. More research is needed to examine students' understanding of APOS theory. This research analyzes prospective mathematics teacher students' understanding of function derivatives based on mathematics anxiety. This study used a qualitative-exploratory design to describe the understanding of function derivatives of prospective mathematics teacher students with APOS theory, considering mathematics anxiety through assignments and interviews. A saturated sample of 26 students was studied. Instruments included math anxiety questionnaires, math ability tests, and function derivative tasks. Data was analyzed using triangulation, peer debriefing, member checking, data reduction, presentation, conclusion, and verification. The study of function derivatives, based on APOS Theory, integrates mental structures and mechanisms like encapsulation and coordination, showing proficiency in simple function derivatives and composition function derivatives but challenges with graphing function derivatives. This research emphasizes the need for teaching strategies that address math anxiety to improve conceptual understanding. It encourages further study of teaching interventions, emotional support, and the long-term impact of math anxiety.
Conceptual Rigor of AI-Generated Mathematical Explanations: The Case of Vector Functions Listiawati, Enny; Kartika, Hendra; Arslan, Cigdem
Journal of Research in Science and Mathematics Education Vol. 4 No. 3 (2025): December
Publisher : EDUPEDIA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/jrsme.v4i3.1862

Abstract

Purpose: The rapid rise of generative artificial intelligence has reshaped discussions in mathematics education, particularly regarding the capacity of advanced systems such as ChatGPT and Gemini to support conceptual rigor. This study aims to investigate how these generative AI tools define and explain vector functions, including the procedures for differentiating and integrating them, in order to evaluate their conceptual rigor of ai-generated mathematical explanations and pedagogical potential. Methodology: Employing a qualitative case study design, the research analyzed responses generated by ChatGPT and Gemini to a structured mathematical prompt on vector functions. The explanations were compared with authoritative calculus textbooks using qualitative content analysis and a standardized scoring rubric. Findings: Findings reveal that both systems provide broadly accurate introductory descriptions of vector functions, highlighting their component-wise structure. However, notable gaps emerge in mathematical precision, particularly in specifying domains, ranges, and the formal conditions underlying differentiability and integrability. ChatGPT tends to include intuitive geometric interpretations, whereas Gemini provides concise procedural explanations, yet both models lack the rigorous logical framing found in standard mathematical texts. Despite these limitations, the systems demonstrate consistent procedural accuracy in describing differentiation and integration of vector-valued functions. Significance: The results underscore the educational potential of generative AI while highlighting the need for teachers to critically evaluate AI-generated mathematical content, particularly when these tools are used to support students’ conceptual learning in mathematics. These findings also highlight important implications for AI literacy, instructional design, and future research in mathematics education.
Co-Authors Andita, Anung Arslan, Cigdem Bayutama, Yudistira Dewi, Hefi Rusnita Dwi Juniati Hendra Kartika, Hendra Kadam, Lencana Nurfi’ah Kusumawardani, Dinda Dwi Oksani, Trifena Wening Purwanto, Arina Emy Rooselyna Ekawati Wimpy, W