Belen Ariño-Morera

Mrs. Belen Ariño-Morera

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Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 June 2025 in Education Sciences
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AMAngélica Martínez-Zarzuelo
Angélica Martínez-Zarzuelo
ÁNÁlvaro Nolla
Álvaro Nolla
TRTomas Recio
Tomas Recio
PTPiedad Tolmos Tolmos
Piedad Tolmos Tolmos
Belen Ariño-MoreraBelen Ariño-Morera
Belen Ariño-Morera
Alejandro Gallardo LozanoAlejandro Gallardo Lozano
Alejandro Gallardo Lozano
This paper presents an initial output of the project “Augmented Intelligence in Mathematics Education through Modeling, Automatic Reasoning and Artificial Intelligence (IAxEM-CM/PHS-2024/PH-HUM-383)”. The starting hypothesis of this project is that the use of technological tools, such as mathematical modeling, visualization, automatic reasoning and artificial intelligence, significantly improves the teaching and learning of mathematics, in addition to fostering positive attitudes in students. With this hypothesis in mind, in this article, we describe an investigation that has been developed in initial training courses for mathematics teachers in several universities in Madrid, where students used GeoGebra Discovery automated reasoning tools to explore geometric properties in real objects through mathematical paths. Through these activities, future teachers modeled, conjectured and validated geometric relationships directly on photographs of their environment, with the essential concourse of the automated discovery and verification of geometric properties provided by GeoGebra Discovery. The feedback provided by the students’ answers to a questionnaire concerning this novel approach shows a positive evaluation of the experience, especially in terms of content learning and the practical use of technology. Although technological, pedagogical and disciplinary knowledge is well represented, the full integration of these components (according to the TPACK model) is still incipient. Finally, the formative potential of the approach behind this experience is highlighted in a context where Artificial Intelligence tools have an increasing presence in education, as well as the need to deepen these three kinds of knowledge in similar experiences that articulate them in a more integrated way.

22 January 2024 in Electronic Proceedings in Theoretical Computer Science
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Belen Ariño-MoreraBelen Ariño-Morera
Belen Ariño-Morera
ZKZoltán Kovács
Zoltán Kovács
TRTomas Recio
Tomas Recio
PTPiedad Tolmos Tolmos
Piedad Tolmos Tolmos
We address, through the automated reasoning tools in GeoGebra Discovery, a problem from a regional phase of the Austrian Mathematics Olympiad 2023. Trying to solve this problem gives rise to four different kind of feedback: the almost instantaneous, automated solution of the proposed problem; the measure of its complexity, according to some recent proposals; the automated discovery of a generalization of the given assertion, showing that the same statement is true over more general polygons than those mentioned in the problem; and the difficulties associated to the analysis of the surprising and involved high number of degenerate cases that appear when using the LocusEquation command in this problem. In our communication we will describe and reflect on these diverse issues, enhancing its exemplar role for showing some of the advantages, problems, and current fields of development of GeoGebra Discovery.

12 November 2022 in Revista do Instituto GeoGebra Internacional de São Paulo
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Belen Ariño-MoreraBelen Ariño-Morera
Belen Ariño-Morera
Este estudo mostrará a capacidade (ou incapacidade) da GeoGebra Discovery de lidar com problemas de geometria euclidiana propostos na recente Olimpíada Europeia de Matemática das Meninas (Hungria, 6 a 12 de abril de 2022). Após uma breve introdução ao contexto desta Olimpíada e ao programa GeoGebra Discovery, os problemas serão descritos e será feita uma tentativa de resolvê-los com a GeoGebra Discovery, finalmente apontando a relação entre as dificuldades encontradas pelos membros da equipe e pela GeoGebra, que podem contribuir para o estabelecimento de critérios sobre o interesse (e complexidade) dos resultados obtidos automaticamente.