Big Data Analytics as a Pedagogical Tool in Oceanography Education within Malaysian Secondary Schools

In the big data era, access to large and complex datasets has become more feasible, which could offer new opportunities for education. This study will propose the use of geospatial interactive online visualisation and analysis infrastructure (GIOVANNI) as a pedagogical tool for oceanography education in secondary schools. The focus is on applying GIOVANNI in oceanography education to analyse spatial and temporal changes in sea Surface temperature (SST) over Peninsular Malaysia's maritime area. Monthly Level 3 data (4 km resolution) obtained from MODIS Aqua for the period of July 2002 to May 2025 was used. Monthly and seasonal average maps were generated to observe spatial SST patterns. Monthly, seasonal and yearly timeseries constructs reveal a slight uptrend in SST. The highest recorded SST was 31.37°C in May 2010, and the lowest was 26.84°C in December 2008. DJF is showing the lowest seasoning temperature, mainly due to the rainy season over this study area. Regionally, the highest SST was observed over the Malacca Straits. This study addresses common questions regarding SST trends, seasonal shifts, and regional anomalies. Through real data analysis, students could develop a more profound understanding of marine environmental dynamics. Integrating GIOVANNI in geography classrooms could promote critical thinking, spatial literacy and scientific curiosity, which aligns with national educational goals.

Big data, GIOVANNI, Pedagogy, Geography

1. Acker, J. G. (2022). Using the GIOVANNI system to assess and evaluate remotely-sensed and model data variables relevant to public health issues. In Geospatial Technology for Human Well-being and Health (pp. 127-149). Cham: Springer International Publishing. [Google Scholar] [Crossref]

2. Amin, A. R. M., Mardiah, A., Adida, M., Munirah, M., & Iskandar, H. (2023). Investigation of COVID-19 Impact on Aerosol Optical Depth Over Malaysia and Indonesia. Ekológia, 42(1), 1-9. [Google Scholar] [Crossref]

3. Bryant, K., KC, B., O'Dell, J., Acker, J. G., & Liu, Z. (2024, December). Giovanni Futures: Making NASA's accessible, on-demand visualization tool more so. In AGU Fall Meeting Abstracts (Vol. 2024, No. 2127, pp. IN11B-2127). [Google Scholar] [Crossref]

4. Gerasimov, I., Savtchenko, A., Alfred, J., Acker, J., Wei, J., & Binita, K. C. (2024). Bridging the Gap: Enhancing Prominence and Provenance of NASA Datasets in Research Publications. Data Science Journal, 23(1). [Google Scholar] [Crossref]

5. Liu, Z., & Acker, J. (2024, December). Data Integrity Challenges in GIOVANNI. In 2024 AGU Fall Meeting. [Google Scholar] [Crossref]

6. Md Kamaludin, K. F., Jumidali, M. M., Ahmad Sa’ad, F. N., & Mat Amin, A. R. (2022). Remote sensing study of the temperature profile over Penang Island using GIOVANNI system. ESTEEM Academic Journal, 18, 20-30. [Google Scholar] [Crossref]

7. Omokpariola, D. O., Nduka, J. K., Anagboso, M. O., & Omokpariola, P. L. (2025). Long-term trends and chemometric analysis of atmospheric air quality matrices in Nigeria (2003–2023) using GIOVANNI satellite data. Discover Applied Sciences, 7(5), 1-24. [Google Scholar] [Crossref]

8. Wei, J., Acker, J., Hegde, M., & Meyer, D. (2018, June). The Best Educational Tool for Interdisciplinary Earth Science Giovanni. In Asia Oceania Geosciences Society (AOGS) Annual Meeting (No. GSFC-E-DAA-TN57881). [Google Scholar] [Crossref]

9. Wan Kamarudin, W. F., Irwan, Z., Yaafar, M. R., & Mat Amin, A. R. (2022). Lockdown effect on carbon monoxide concentration over Malaysia and Indonesia. Przegląd Naukowy Inżynieria i Kształtowanie Środowiska, 31. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet