Technological Tools Used in Mathematical Thinking at the University Level: A Systematic Review.

• Autores: Román-Castillo, Enny, Becerra Celiz, Edin, Jara Alarcón, Teresa Juliana, Chunga More, Yessica Del Milagro
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional Autónoma de Chota
• Repositorio: UNACH-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.unach.edu.pe:20.500.14142/799
• Enlace del recurso: https://repositorio.unach.edu.pe/handle/20.500.14142/799; https://doi.org/10.36941/jesr-2025-0043
• Nivel de acceso: acceso abierto
• Materia: SOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems science::Information technology; https://purl.org/pe-repo/ocde/ford#2.02.03

This systematic review analyzes the technological tools used in mathematical thinking at the university level, examining their impact and effectiveness in enhancing students' mathematical comprehension and problem-solving abilities. Through a comprehensive analysis of 125 scientific articles published between 2019 and 2024 and retrieved from the Scopus, SciELO, and Web of Science databases, the study identified key technological tools and their implications for mathematical education. The methodology followed PRISMA guidelines, incorporating bibliometric analysis via VOSviewer software to map collaboration networks and research trends. The results revealed five fundamental tools that transform mathematics teaching: MATLAB, GeoGebra, Mathematica, Python, and Khan Academy. The bibliometric analysis revealed a significant concentration of research in the United States and Europe, with growing contributions from Latin American countries. The findings indicate that effective technological tools combine three essential elements: mathematical visualization capability, interactivity, and immediate feedback. Furthermore, the study revealed a significant relationship between technological tool implementation and the development of higher-order thinking skills in university students. Geographic analysis revealed research gaps in underrepresented regions, suggesting the need for more inclusive studies. Key limitations include the rapid evolution of technological tools, which may affect the long-term applicability of findings, and the predominance of studies from well-resourced educational contexts, potentially limiting generalizability to resourceconstrained settings. Future research should focus on investigating the effectiveness of these tools in diverse socioeconomic contexts, exploring emerging technologies such as artificial intelligence in mathematical education, and conducting longitudinal studies to assess their long-term impact on student learning outcomes. This review contributes to understanding how technological tools can be strategically integrated into university mathematics education, considering both pedagogical frameworks and technological infrastructure requirements.