Accelerating Students' Environmental Knowledge Creation through Digital Transformation: A Cloud-Native Geospatial Protocol for Soil Erosion Management

Authors

DOI:

https://doi.org/10.30998/0zdyfn87

Keywords:

cloud, digital, environmental sciences, RUSLE, STEM

Abstract

The application of cloud-native geospatial protocol in environmental science learning is novel and rare, warranting further in-depth study. This study aims to analyze cloud-native geospatial protocol for soil erosion management as part of the environmental science courses. This study employed a Design-Based Research (DBR) approach within the STEM learning framework. This study focused on prototyping the learning materials and validating their effectiveness through simulation the Revised Universal Soil Loss Equation (RUSLE) model hosted on Google Earth Engine. The Cloud-Native Geospatial Protocol serves as a valid and effective instructional material for environmental sciences learning. It successfully accelerates environmental knowledge creation by transforming abstract STEM concepts into visual and quantifiable experiences. This learning model simulates the annual reduction in soil erosion risk, providing a concrete context for mathematical and engineering reasoning in solving environmental problems.

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References

Alamri, S. (2024). The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics. ISPRS International Journal of Geo-Information, 13(6), 168. https://doi.org/10.3390/ijgi13060168

Aldhafeeri, Fayiz M, & Alotaibi, Asmaa A. (2023). Reimagining Education for Successful and Sustainable Digital Shifting. Sage Open, 13(1), 21582440231154470. https://doi.org/10.1177/21582440231154474

Banerjee, S., Szirony, G. M., McCune, N., Davis, W. S., Subocz, S., & Ragsdale, B. (2022). Transforming Social Determinants to Educational Outcomes: Geospatial Considerations. Healthcare (Basel, Switzerland), 10(10). https://doi.org/10.3390/healthcare10101974

Bernhäuserová, V., Havelková, L., Hátlová, K., & Hanus, M. (2022). The Limits of GIS Implementation in Education: A Systematic Review. ISPRS International Journal of Geo-Information, 11(12), 592. https://doi.org/10.3390/ijgi11120592

Bufasi, E., Hoxha, M., Cuka, K., & Vrtagic, S. (2022). Developing Student’s Comprehensive Knowledge of Physics Concepts by Using Computational Thinking Activities: Effects of a 6-Week Intervention. International Journal of Emerging Technologies in Learning, 17(18), 161–176. https://doi.org/10.3991/ijet.v17i18.31743

Cheung, S. K. S., Kwok, L. F., Phusavat, K., & Yang, H. H. (2021). Shaping the future learning environments with smart elements: challenges and opportunities. In International journal of educational technology in higher education (Vol. 18, Issue 1, p. 16). https://doi.org/10.1186/s41239-021-00254-1

Christensen, D. (2023). Computational Thinking to Learn Environmental Sustainability: A Learning Progression. Journal of Science Education and Technology, 32(1), 26–44. https://doi.org/10.1007/s10956-022-10004-1

Dominguez, A., De la Garza, J., Quezada-Espinoza, M., & Zavala, G. (2024). Integration of Physics and Mathematics in STEM Education: Use of Modeling. Education Sciences, 14(1), 20. https://doi.org/10.3390/educsci14010020

Drozdowski, C. S., Emeghara, S., Marlowe, T. J., Herbert, K. G., Anu, V. K., Hagiwara, S., & Robila, S. A. (2024). The APP Method: Self-Regulation Strategies Giving POWER to Computer Science Students. 2024 IEEE Integrated STEM Education Conference, ISEC 2024. https://doi.org/10.1109/ISEC61299.2024.10664967

Erdoǧan, M. A., Esbah, H., & Berberoglu, S. (2016). Erosion risk mapping using rusle with GIS: Case study of Büyük Menderes river basin of Turkey. International Journal of Safety and Security Engineering, 6(2), 132–140. https://doi.org/10.2495/SAFE-V6-N2-132-140

FreeWheel Biz-UI Team. (2025). Cloud-Native Application Architecture_ Microservice Development Best Practice _ Springer Nature Link. Springer Singapore. https://doi.org/10.1007/978-981-19-9782-2

Giuliani, F., Gaglio, F., Martino, M., & De Falco, A. (2024). A HBIM pipeline for the conservation of large-scale architectural heritage: the city Walls of Pisa. Heritage Science, 12(1), 35. https://doi.org/10.1186/s40494-024-01141-4

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/https://doi.org/10.1016/j.susoc.2022.05.004

Hochschild, V., Märker, M., Rodolfi, G., & Staudenrausch, H. (2003). Delineation of erosion classes in semi-arid southern African grasslands using vegetation indices from optical remote sensing data. Hydrological Processes, 17(5), 917–928. https://doi.org/10.1002/hyp.1170

Jeddoub, I., Nys, G.-A., Hajji, R., & Billen, R. (2025). Data integration across urban digital twin lifecycle: a comprehensive review of current initiatives. Annals of GIS, 31(3), 367–386. https://doi.org/10.1080/19475683.2024.2416135

Kanaki, K., Kalogiannakis, M., Poulakis, E., & Politis, P. (2022). Investigating the Association between Algorithmic Thinking and Performance in Environmental Study. Sustainability (Switzerland), 14(17). https://doi.org/10.3390/su141710672

Kandemir, M. A., & Eryilmaz, N. (2025). Innovative approaches in mathematical modeling: Harnessing technology for teaching second degree equations to Future Mathematics Educators in Türkiye. Social Sciences & Humanities Open, 11, 101281. https://doi.org/https://doi.org/10.1016/j.ssaho.2025.101281

Kurniawan, W., Anwar, K., Jufrida, J., Kamid, K., & Riantoni, C. (2025). Personalized Digital Learning Environment with Differentiated Instruction to Foster Computational Thinking in Robotics Education. Journal of Information Technology Education: Innovations in Practice, 24, 004. https://doi.org/10.28945/5427

Lai, C.-H., & Lin, C.-Y. (2025). Analysis of Learning Behaviors and Outcomes for Students with Different Knowledge Levels: A Case Study of Intelligent Tutoring System for Coding and Learning (ITS-CAL). Applied Sciences, 15(4), 1922. https://doi.org/10.3390/app15041922

Li, H.-C. (2025). STEM education and sustainability: what role can mathematics education play in the era of climate change? Research in Mathematics Education, 27(2), 291–313. https://doi.org/10.1080/14794802.2025.2499813

Li, X., Yue, J., Wang, S., Luo, Y., Su, C., Zhou, J., Xu, D., & Lu, H. (2024). Development of Geographic Information System Architecture Feature Analysis and Evolution Trend Research. Sustainability, 16(1), 137. https://doi.org/10.3390/su16010137

Lim, C., Han, Y., Chae, J., Eom, T., & Park, S. (2025). The impact of one-to-one technology for improving digital literacy at middle schools in South Korea. Asia Pacific Education Review. https://doi.org/10.1007/s12564-025-10091-w

Mena-Guacas, A. F., López-Catalán, L., Bernal-Bravo, C., & Ballesteros-Regaña, C. (2025). Educational transformation through emerging technologies: critical review of scientific impact on learning. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030368

Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), e16348. https://doi.org/10.1016/j.heliyon.2023.e16348

Mkhitaryan, K., Sanamyan, A., Mnatsakanyan, M., Kirakosyan, E., & Ratner, S. (2025). Integrating AI and Geospatial Technologies for Sustainable Smart City Development: A Case Study of Yerevan. Urban Science, 9(10), 389. https://doi.org/10.3390/urbansci9100389

Naseer, F., Khan, M. N., Tahir, M., Addas, A., & Aejaz, S. M. H. (2024). Integrating deep learning techniques for personalized learning pathways in higher education. Heliyon, 10(11), e32628. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e32628

Onungwa, Ihuoma, Olugu-Uduma, Nnezi, & Shelden, Dennis R. (2021). Cloud BIM Technology as a Means of Collaboration and Project Integration in Smart Cities. Sage Open, 11(3), 21582440211033250. https://doi.org/10.1177/21582440211033250

Prangon, N. F., & Wu, J. (2024). AI and Computing Horizons: Cloud and Edge in the Modern Era. In Journal of Sensor and Actuator Networks (Vol. 13, Issue 4, p. 44). https://doi.org/10.3390/jsan13040044

Raj, P., Vanga, S., & Chaudhary, A. (2022). Front Matter. In Cloud‐Native Computing (pp. i–xix). https://doi.org/10.1002/9781119814795.fmatter

Renard, K., Foster, G., Weesies, G., McCool, & Yoder, D. (1997). Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). US Department of Agriculture, Agriculture Handbook No.703.

Safaah, E., & Karyaningsih, D. (2020). Applying Computational Thinking in Unsera Students used Online Calculus Training. In V. S., M. M.B., L. C.-H., G. T.L., M. null, & S. A. (Eds.), Journal of Physics: Conference Series (Vol. 1477, Issue 2). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1477/2/022001

Saidin, N. D., Khalid, F., Martin, R., Kuppusamy, Y., & Munusamy, N. A. P. (2021). Benefits and challenges of applying computational thinking in education. International Journal of Information and Education Technology, 11(5), 248–254. https://doi.org/10.18178/ijiet.2021.11.5.1519

Samodra, I., Rahmawati, F., & Prayitno, B. A. (2025). Driving Question Formulation in Green Chemistry: A Computational Thinking Approach. Journal of Physics: Conference Series, 3148(1). https://doi.org/10.1088/1742-6596/3148/1/012016

Siller, H.-S., Vorhölter, K., & Just, J. (2025). Problem Posing as a Way of Promoting Individual Mathematical Thinking in STEM Contexts – The Case of Climate Change. International Journal of Science and Mathematics Education, 23(6), 2113–2133. https://doi.org/10.1007/s10763-024-10518-7

Souza, A. S. C. de, & Debs, L. (2024). Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review. Social Sciences & Humanities Open, 9, 100902. https://doi.org/10.1016/j.ssaho.2024.100902

Tarolli, P., & Straffelini, E. (2020). Agriculture in Hilly and Mountainous Landscapes: Threats, Monitoring and Sustainable Management. Geography and Sustainability, 1(1), 70–76. https://doi.org/10.1016/j.geosus.2020.03.003

Thakur, J. K., Singh, S. K., & Ekanthalu, V. S. (2017). Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling. Applied Water Science, 7(4), 1595–1608. https://doi.org/10.1007/s13201-016-0384-5

Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8

Tramonti, M., Dochshanov, A. M., Fiadotau, M., Grönlund, M., Callaghan, P., Ailincai, A., Marini, B., Joenvaara, S., Maurer, L., & Delle Donne, E. (2024). Game on for Climate Action: Big Game Delivers Engaging STEM Learning. Education Sciences, 14(8), 893. https://doi.org/10.3390/educsci14080893

Vance, T. C., Wengren, M., Burger, E., Hernandez, D., Kearns, T., Medina-lopez, E., Merati, N., Brien, K. O., Neil, J. O., Potemra, J. T., Signell, R. P., Wilcox, K., & Vance, T. C. (2019). From the Oceans to the Cloud : Opportunities and Challenges for Data , Models , Computation and Workflows. Frontiers in Marine Science, 6(211), 1–18. https://doi.org/10.3389/fmars.2019.00211

VoPham, T., White, A. J., & Jones, R. R. (2024). Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era. Cancer Epidemiology, Biomarkers & Prevention : A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 33(4), 451–460. https://doi.org/10.1158/1055-9965.EPI-23-1237

Wang, C., Chen, X., Yu, T., Liu, Y., & Jing, Y. (2024). Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanities and Social Sciences Communications, 11(1), 256. https://doi.org/10.1057/s41599-024-02717-y

Weinhandl, R., Baldinger, S., & Kapplmüller, M. (2025). Educational design characteristics of digital mathematics learning environments from a student perspective. International Journal of Mathematical Education in Science and Technology, 2025, 1–27. https://doi.org/10.1080/0020739X.2025.2562275

Wischmeier, W. H., & Smith, O. D. (1978). Predicting rainfall erosion losses-a guide to corservation planning. U.S. Department of Agriculture, Agriculture Handbook No. 537.

Yang, C., Yu, M., Hu, F., Jiang, Y., & Li, Y. (2017). Utilizing Cloud Computing to address big geospatial data challenges. Computers, Environment and Urban Systems, 61, 120–128. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2016.10.010

Yu, J., Bekerian, D. A., & Osback, C. (2024). Navigating the Digital Landscape: Challenges and Barriers to Effective Information Use on the Internet. Encyclopedia, 4(4), 1665–1680. https://doi.org/10.3390/encyclopedia4040109

Zhang, H., Wang, Z., Jiang, R., & Wu, H. (2025). Exploring the Influence of Technology- Enhanced Active Learning Environments on Pre-Service Teachers ’ TPACK and Technology Beliefs. SAGE Open, 2025(5268), 1–24. https://doi.org/10.1177/21582440251359823

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Published

2026-03-31

How to Cite

Husamah, H., Romadoni, L. S., Rahardjanto, A., & Hadi, S. (2026). Accelerating Students’ Environmental Knowledge Creation through Digital Transformation: A Cloud-Native Geospatial Protocol for Soil Erosion Management. Formatif : Jurnal Ilmiah Pendidikan MIPA, 16(1), 133-146. https://doi.org/10.30998/0zdyfn87