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Deep Learning Trajectories for Statistical Graphs in Mathematics Learning: Integration of SOLO Taxonomy and Ethnomathematics Using GeoGebra

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Title: Deep Learning Trajectories for Statistical Graphs in Mathematics Learning: Integration of SOLO Taxonomy and Ethnomathematics Using GeoGebra
Language: English
Authors: Rahmi Ramadhani, Soeharto Soeharto, Rully Charitas Indra Prahmana, Guillermo Bautista, Iwan Fitrianto Rahmad, Muhammad Zainur Rifai, Fitria Arifiyanti, Zsolt Lavicza
Source: Discover Education. 2026 5.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Learning Trajectories, Statistics, Graphs, Mathematics Education, Taxonomy, Ethnology, Mathematics, Computer Software, Junior High School Students, Foreign Countries, Culturally Relevant Education
Geographic Terms: Indonesia
DOI: 10.1007/s44217-026-01711-7
ISSN: 2731-5525
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.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1518322
Database: ERIC
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  Data: Deep Learning Trajectories for Statistical Graphs in Mathematics Learning: Integration of SOLO Taxonomy and Ethnomathematics Using GeoGebra
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  Data: <searchLink fieldCode="AR" term="%22Rahmi+Ramadhani%22">Rahmi Ramadhani</searchLink><br /><searchLink fieldCode="AR" term="%22Soeharto+Soeharto%22">Soeharto Soeharto</searchLink><br /><searchLink fieldCode="AR" term="%22Rully+Charitas+Indra+Prahmana%22">Rully Charitas Indra Prahmana</searchLink><br /><searchLink fieldCode="AR" term="%22Guillermo+Bautista%22">Guillermo Bautista</searchLink><br /><searchLink fieldCode="AR" term="%22Iwan+Fitrianto+Rahmad%22">Iwan Fitrianto Rahmad</searchLink><br /><searchLink fieldCode="AR" term="%22Muhammad+Zainur+Rifai%22">Muhammad Zainur Rifai</searchLink><br /><searchLink fieldCode="AR" term="%22Fitria+Arifiyanti%22">Fitria Arifiyanti</searchLink><br /><searchLink fieldCode="AR" term="%22Zsolt+Lavicza%22">Zsolt Lavicza</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Discover+Education%22"><i>Discover Education</i></searchLink>. 2026 5.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: <searchLink fieldCode="DE" term="%22Indonesia%22">Indonesia</searchLink>
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  Data: 10.1007/s44217-026-01711-7
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  Data: 2731-5525
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  Data: 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.
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        Value: 10.1007/s44217-026-01711-7
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      – Text: English
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      – SubjectFull: Learning Trajectories
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      – SubjectFull: Statistics
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      – SubjectFull: Graphs
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