Benefits of Big Data in Supporting Better Educational Decision Making
Downloads
Big data plays an increasingly important role in education, offering great potential to improve educational decision making. Big Data collects, stores, and analyzes large amounts of and diverse data at very high speeds. In the educational context, big data collects data from various sources, including school management systems, student academic records, student satisfaction surveys, and online data. Analysis of this data can provide valuable information to decision makers in the education sector to identify relevant trends, patterns and opportunities. This research discusses the great benefits that can be gained from using Big Data in an educational context to support better decision making. Through research on the educational benefits of big data in supporting better decision making, it is hoped that this will provide an excellent opportunity to improve decision making in education. Through careful data analysis, education can become more effective, efficient and relevant to better meet the needs of students and society. The method used in this research is a quantitative method. Researchers conducted a survey using a Google form consisting of 15 statements related to the title of the research. Researchers found that using big data in education provides great opportunities to improve better educational decision making. And supported by careful selection as material for consideration. The limitation of this research is that the researcher only conducted research in schools and the researcher did not conduct research directly in schools but shared a survey link on a Google form containing a statement about the benefits of big data in supporting better educational decision making.
Aceto, G., Persico, V., & Pescape, A. (2020). Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0. Journal of Industrial Information Integration, 18, 100129. https://doi.org/10.1016/j.jii.2020.100129
Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2019). code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages, 3(POPL), 1–29. https://doi.org/10.1145/3290353
Baugh, L. R. (2001). Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Research, 29(5), 29e–229. https://doi.org/10.1093/nar/29.5.e29
Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2
Butun, I., Osterberg, P., & Song, H. (2020). Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures. IEEE Communications Surveys & Tutorials, 22(1), 616–644. https://doi.org/10.1109/COMST.2019.2953364
Chen, C. L. P., & Liu, Z. (2018). Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture. IEEE Transactions on Neural Networks and Learning Systems, 29(1), 10–24. https://doi.org/10.1109/TNNLS.2017.2716952
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 54. https://doi.org/10.1186/s40537-019-0217-0
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource?Based View and Big Data Culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134, 109761. https://doi.org/10.1016/j.chaos.2020.109761
Fang, C., Li, J., Zhang, M., Zhang, Y., Yang, F., Lee, J.Z., Lee, M.-H., Alvarado, J., Schroeder, M.A., Yang, Y. , Lu, B., Williams, N., Ceja, M., Yang, L., Cai, M., Gu, J., Xu, K., Wang, X., & Meng, Y.S. (2019). Quantifying inactive lithium in lithium metal batteries. Nature, 572(7770), 511–515. https://doi.org/10.1038/s41586-019-1481-z
Fozouni, P., Son, S., Díaz De León Derby, M., Knott, GJ, Gray, CN, D'Ambrosio, MV, Zhao, C., Switz, N.A., Kumar, GR, Stephens, SI, Boehm , D., Tsou, C.-L., Shu, J., Bhuiya, A., Armstrong, M., Harris, A.R., Chen, P.-Y., Osterloh, J.M., Meyer-Franke, A., … Ott, M. (2021). Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy. Cell, 184(2), 323-333.e9. https://doi.org/10.1016/j.cell.2020.12.001
Frank, A.G., Dalenogare, L.S., & Ayala, N.F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. https://doi.org/10.1016/j.ijpe.2019.01.004
Liang, C., Amelung, W., Lehmann, J., & Kästner, M. (2019). Quantitative assessment of microbial necromass contribution to soil organic matter. Global Change Biology, 25(11), 3578–3590. https://doi.org/10.1111/gcb.14781
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/TKDE.2018.2876857
Lu, Y., Liu, C., Wang, KI-K., Huang, H., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837. https://doi.org/10.1016/j.rcim.2019.101837
Moon, KR, Van Dijk, D., Wang, Z., Gigante, S., Burkhardt, DB, Chen, WS, Yim, K., Elzen, AVD, Hirn, MJ, Coifman, RR, Ivanova, NB, Wolf , G., & Krishnaswamy, S. (2019). Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology, 37(12), 1482–1492. https://doi.org/10.1038/s41587-019-0336-3
Ngiam, KY, & Khor, IW (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
Saiz-Rubio, V., & Rovira-Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10(2), 207. https://doi.org/10.3390/agronomy10020207
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(09), 1426–1448. https://doi.org/10.1017/S0033291719000151
Tao, F., Qi, Q., Wang, L., & Nee, AYC (2019). Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014
Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020). Deep learning on image denoising: An overview. Neural Networks, 131, 251–275. https://doi.org/10.1016/j.neunet.2020.07.025
Ting, DSW, Carin, L., Dzau, V., & Wong, T.Y. (2020). Digital technology and COVID-19. Nature Medicine, 26(4), 459–461. https://doi.org/10.1038/s41591-020-0824-5
Vinayakumar, R., Alazab, M., Soman, K.P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access, 7, 41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334
Xu, L.D., & Duan, L. (2019). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems, 13(2), 148–169. https://doi.org/10.1080/17517575.2018.1442934
Yang, F., Yang, H., Fu, J., Lu, H., & Guo, B. (2020). Learning Texture Transformer Network for Image Super-Resolution. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 5790–5799. https://doi.org/10.1109/CVPR42600.2020.00583
Yu, F., Yan, L., Wang, N., Yang, S., Wang, L., Tang, Y., Gao, G., Wang, S., Ma, C., Xie, R., Wang, F., Tan, C., Zhu, L., Guo, Y., & Zhang, F. (2020). Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clinical Infectious Diseases, 71(15), 793–798. https://doi.org/10.1093/cid/ciaa345
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https://doi.org/10.1016/j.rse.2020.111716
Zhang, C., Patras, P., & Haddadi, H. (2019). Deep Learning in Mobile and Wireless Networking: A Survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287. https://doi.org/10.1109/COMST.2019.2904897
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R.X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q.V. (2018). Learning Transferable Architectures for Scalable Image Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8697–8710. https://doi.org/10.1109/CVPR.2018.00907
Zou, H., Guo, L., Xue, H., Zhang, Y., Shen, X., Liu, X., Wang, P., He, X., Dai, G., Jiang, P., Zheng, H., Zhang, B., Xu, C., & Wang, Z. L. (2020). Quantifying and understanding the triboelectric series of inorganic non-metallic materials. Nature Communications, 11(1), 2093. https://doi.org/10.1038/s41467-020-15926-1
Copyright (c) 2024 Efendi Efendi, Agry Alfiah, Fathul Qorib, Fadli Firdaus, Sabri Sabri

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.