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: | 2026 |
| Accession Number: | EJ1518322 |
| Database: | ERIC |
| 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. |
|---|---|
| ISSN: | 2731-5525 |
| DOI: | 10.1007/s44217-026-01711-7 |
