Deep learning trajectories for statistical graphs in mathematics learning: integration of SOLO taxonomy and ethnomathematics using GeoGebra
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Resumo
Abstract This study develops and validates of a Learning Trajectory (LT) for statistical graph instruction by integrating a Deep Learning (DL) approach with the Structure of the Observed Learning Outcome (SOLO) taxonomy, ethnomathematics, and technology. Using an Educational Design Research (EDR) methodology, the study was conducted through pilot teaching, design experimentation, and retrospective analysis. A total of 190 final-year junior high school students from Binjai City, North Sumatra, Indonesia, participated. Data were collected through classroom observations and analyzed using the SOLO taxonomy to trace students’ conceptual development. The Hypothetical Learning Trajectory (HLT) was iteratively compared with the Actual Learning Trajectory (ALT) to produce a validated LT, which was further developed into a Local Instructional Theory (LIT). The results show that students’ understanding progressed from Unistructural to Extended Abstract levels, particularly in interpreting and generalizing graphs and mean values. The integration of ethnomathematics through the Berahoi tradition, combined with GeoGebra and AI, supported meaningful and culturally relevant learning. Importantly, the findings reveal that conceptual progression was not driven by contextual or technological elements alone, but by how these were systematically integrated within a learning trajectory supported by targeted scaffolding. Theoretically, this study contributes to EDR by providing an empirically grounded explanation of how learning trajectories mediate students’ progression across SOLO levels. Practically, it offers a framework for designing contextual, technology-enhanced, and culturally responsive mathematics instruction.
Dados da obra
- Tipo
- Artigo
- Periódico
- Discover Education, v. 5, n. 1
- Editora
- Springer
- Ano
- 2026 (2026-06)
- Idioma
- en
- Palavras-chave
- Deep learning; Ethnomathematics; GeoGebra; Learning trajectory; SOLO taxonomy; Learning Trajectories; Statistics; Graphs; Mathematics Education; Taxonomy; Ethnology; Mathematics; Computer Software; Junior High School Students; Foreign Countries; Culturally Relevant Education
- Temas
- Etnomatemática, Tecnologias Digitais
- DOI
- 10.1007/s44217-026-01711-7
- ISSN
- 2731-5525
- Acesso
- Acesso aberto
- Fontes
- DOAJ — Directory of Open Access Journals; ERIC — Education Resources Information Center
- Na Biblioteca desde
- 01/10/2026 · verificada em 03/10/2026
Como citar
ABNT
RAMADHANI, Rahmi et al.. Deep learning trajectories for statistical graphs in mathematics learning: integration of SOLO taxonomy and ethnomathematics using GeoGebra. Discover Education, v. 5, n. 1, 2026. DOI: https://doi.org/10.1007/s44217-026-01711-7.
APA
Ramadhani, R., Soeharto, S., Prahmana, R. C. I., Bautista, G., Rahmad, I. F., Rifai, M. Z., Arifiyanti, F., & Lavicza, Z. (2026). Deep learning trajectories for statistical graphs in mathematics learning: integration of SOLO taxonomy and ethnomathematics using GeoGebra. Discover Education, 5(1). https://doi.org/10.1007/s44217-026-01711-7
BibTeX
@article{ramadhani2026,
author = {Ramadhani, Rahmi and Soeharto, Soeharto and Prahmana, Rully Charitas Indra and Bautista, Guillermo and Rahmad, Iwan Fitrianto and Rifai, Muhammad Zainur and Arifiyanti, Fitria and Lavicza, Zsolt},
title = {Deep learning trajectories for statistical graphs in mathematics learning: integration of SOLO taxonomy and ethnomathematics using GeoGebra},
year = {2026},
journal = {Discover Education},
volume = {5},
number = {1},
publisher = {Springer},
doi = {10.1007/s44217-026-01711-7},
language = {en}
}RIS
TY - JOUR AU - Ramadhani, Rahmi AU - Soeharto, Soeharto AU - Prahmana, Rully Charitas Indra AU - Bautista, Guillermo AU - Rahmad, Iwan Fitrianto AU - Rifai, Muhammad Zainur AU - Arifiyanti, Fitria AU - Lavicza, Zsolt TI - Deep learning trajectories for statistical graphs in mathematics learning: integration of SOLO taxonomy and ethnomathematics using GeoGebra PY - 2026 JO - Discover Education VL - 5 IS - 1 DO - 10.1007/s44217-026-01711-7 LA - en ER -
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