Creative Geometry through Roof Modeling: Enhancing Angle Understanding via Deep Learning

Puspa Herliana; Hendry Hermawan; Sulistiyana Sulistiyana; Aslamiah Aslamiah; Ahmad Suriansyah

doi:10.31764/jtam.v9i4.33710

Creative Geometry through Roof Modeling: Enhancing Angle Understanding via Deep Learning

Puspa Herliana, Hendry Hermawan, Sulistiyana Sulistiyana, Aslamiah Aslamiah, Ahmad Suriansyah

Abstract

This study aimed to evaluate the effectiveness of deep learning-based geometry instruction using miniature roof construction in improving students’ understanding of angles. A quasi-experimental design was conducted at SMPN 1 Kotabaru with 64 seventh-grade students divided into experimental and control groups. The experimental group participated in eight sessions of project-based learning that integrated contextual modeling and collaborative exploration, while the control group received conventional textbook-based instruction. Students’ comprehension of angle concepts including classification, measurement, and application was assessed using a validated geometry test and structured reflection journals. The results showed that students in the experimental group demonstrated significantly greater improvement in angle understanding compared to the control group. Statistical analysis confirmed the effectiveness of the intervention, with a large performance gap favoring the experimental group. These findings suggest that deep learning strategies, when combined with hands-on modeling and contextual relevance, can substantially enhance conceptual mastery in geometry education.

Keywords

Deep Learning; Geometry Instruction; Angle Comprehension; Quasi-Experiment; Roof Modeling.

Full Text:

DOWNLOAD [PDF]

References

Balestriero, R., Humayun, A. I., & Baraniuk, R. (2025). On the geometry of deep learning. American Mathematical Society. https://doi.org/https://doi.org/10.48550/arXiv.2408.04809

Dudley, R. (2012). The Shapiro-Wilk Test For Normality. 465, 1–7.

Furner, J. M. (2024). Creating Connections: Using Problem-Based Instruction with Mathematics and Technology like GeoGebra for STEM Integration. International Journal of Education in Mathematics, Science and Technology, 12(4), 957–970. https://doi.org/10.46328/ijemst.4018

Gerken, J. E., Aronsson, J., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., & Persson, D. (2023). Geometric deep learning and equivariant neural networks. Artificial Intelligence Review, 56(12), 14605–14662. https://doi.org/10.1007/s10462-023-10502-7

Heidari, N., & Iosifidis, A. (2025). Geometric deep learning for computer-aided design: A survey. ArXiv Preprint. https://doi.org/https://doi.org/10.48550/arXiv.2402.17695

Himmi, N., Armanto, D., & Amry, Z. (2025). Implementation of Project Based Learning (PjBL) in Mathematics Education: A Systematic Analysis of International Practices and Theoretical Foundations. Science Insights Education Frontiers, 26(2), 4305–4321. https://doi.org/10.15354/sief.25.or699

Kallogjeri, D., & Piccirillo, J. F. (2023). A Simple Guide to Effect Size Measures. JAMA Otolaryngology–Head & Neck Surgery, 149(5), 447. https://doi.org/10.1001/jamaoto.2023.0159

Kasi, Y. F., Bai, D. V., Novia, N., Deporos, S. R. C., & Mababaya, D. (2025). Implementation of deep learning in school curriculum: Perspectives of teachers in Nagekeo Regency. Paedagogie, 28(2), 320–328. https://doi.org/https://doi.org/10.20961/paedagogia.v28i2.103377

Kim, D. Y., & Sung-Hyun, C. (2020). A qualitative case study on challenges and strategies for a open recruitment principal in school management. In Journal of education and culture (Vol. 26, pp. 353–376). https://www.semanticscholar.org/paper/111565b4edb7dadc6913173c3580e4a58ead4fe8

Li, W., Coelen, R., & Otten, S. (2025). Enhancing interaction quality in interdisciplinary group work: The impact of student motivation and teacher-initiated diversity climate. Social Psychology of Education, 28(1), 41. https://doi.org/10.1007/s11218-024-10004-6

Maciejewski, M. L. (2020). Quasi-experimental design. Biostatistics & Epidemiology, 4(1), 38–47. https://doi.org/10.1080/24709360.2018.1477468

Makamure, C. (2025). Project-Based Learning (PBL) for Professional Development of Mathematics Educators: Unlocking Opportunities and Navigating Pitfalls and Considerations. In Teacher Training and Student Learning - Past Values, Present Uncertainties and Future Prospects [Working Title]. IntechOpen. https://doi.org/10.5772/intechopen.1009552

Masuku, M. M., Jili, N. N., & Sabela, P. T. (2020). Assessment as A Pedagogy and Measuring Tool in Promoting Deep Learning In Institutions of Higher Learning. International Journal of Higher Education, 10(2), 274. https://doi.org/10.5430/ijhe.v10n2p274

Moral-Sánchez, S. N., Sánchez-Compaña, M. . T., & Romero, I. (2022). Geometry with a STEM and Gamification Approach: A Didactic Experience in Secondary Education. Mathematics, 10(18), 3252. https://doi.org/10.3390/math10183252

Niar, A. R. (2025). Systematic literature review: Improving creative thinking ability and curiosity in learning geometry GeoGebra assistance. Prosiding Seminar Nasional Mahasiswa Matematika Unnes. https://proceeding.unnes.ac.id/psnmmu/article/view/4269

Odoi, B., Samita, S., Al-Hassan, S., & Twumasi-Ankrah, S. (2019). Efficiency of bartlett and levenes tests for testing homogeneity of variance under varying number of replicates and groups in one – way ANOVA. International Journal of Innovative Technology and Exploring Engineering, 8(6 Special Issue 4), 1219–1223. https://doi.org/10.35940/ijitee.F1250.0486S419

Ponte, R., Viseu, F., Neto, T. B., & Aires, A. P. (2023). Revisiting manipulatives in the learning of geometric figures. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1217680

Şener, A., Poçan, S., & Ergen, B. (2025). Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization. Türk Doğa ve Fen Dergisi, 14(2), 247–258. https://doi.org/10.46810/tdfd.1639446

Setiabudhi, H., Suwono, Setiawan, Y. A., & Karim, S. (2024). Analisis Data Kuantitatif dengan Smart PLS 4 (I. P. H. Duari (ed.)). Borneo Novelty Publishing.

Stapor, K. (2020). Descriptive and inferential statistics. In Introduction to probabilistic and statistical methods with examples in R (pp. 63–131). Springer.

Sudirtha, I. G., Widiana, I. W., & Adijaya, M. A. (2022). The Effectiveness of Using Revised Bloom’s Taxonomy-Oriented Learning Activities to Improve Students’ Metacognitive Abilities. Journal of Education and E-Learning Research, 9(2), 55–61. https://doi.org/10.20448/jeelr.v9i2.3804

Tytler, R., Anderson, J., & Williams, G. (2023). Exploring a framework for integrated STEM: challenges and benefits for promoting engagement in learning mathematics. ZDM – Mathematics Education, 55(7), 1299–1313. https://doi.org/10.1007/s11858-023-01519-x

Weigand, H.-G., Hollebrands, K., & Maschietto, M. (2025). Geometry education at secondary level – a systematic literature review. ZDM – Mathematics Education, 57(4), 829–843. https://doi.org/10.1007/s11858-025-01703-1

Xu, C., Wu, C.-F., Xu, D.-D., Lu, W.-Q., & Wang, K.-Y. (2022). Challenges to Student Interdisciplinary Learning Effectiveness: An Empirical Case Study. Journal of Intelligence, 10(4), 88. https://doi.org/10.3390/jintelligence10040088

Zhu, Y., Shen, H., Wen, A., Gao, Z., Qin, Q., Shan, S., Li, W., Fu, X., Zhao, Y., & Wang, Y. (2025). [Deep learning algorithms for intelligent construction of a three-dimensional maxillofacial symmetry reference plane]. In Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences (Vol. 57, Issue 1, pp. 113–120). https://www.semanticscholar.org/paper/add115eb5c9c766e7b3ef7b88f64f454d10626b8

DOI: https://doi.org/10.31764/jtam.v9i4.33710