Abstract
Nowadays, educational institutions are one of the biggest producers of data. The rise of e-Learning contents, digital libraries, webinars, learning management systems, online classes and examinations, video surveillance, sensors, and wearables devices contribute to this data explosion. Learning management systems can index millions of students’ data, their interactions, course registrations, social networks, and their Internet research results. Besides, the potential to learn from this population-scale data is massive. By building analytic dashboards using machine learning and deep learning approaches on these datasets, educational organizations can improve the learning experience, teaching skills, and learning environment and drive better teaching and learning outcomes. Some real-world examples are students’ dropouts, students’ behavior, employee and student's health, prevention fraud data and abuse, etc. In present legacy systems, the data silos from the data warehouse could not handle unstructured data. It increases the complexity and cost of transferring data between multiple disparate data systems. Also, there is a performance bottleneck with data throughput while managing multiple data copies in different locations. This paper aims to store all educational data in a central location and handle all structured and unstructured data without any performance bottlenecks. It is proposed to design an enterprise data lake solution for academic organizations using deep learning to predict the outcomes.
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Kuppusamy, P., Suresh Joseph, K. (2022). Building an Enterprise Data Lake for Educational Organizations for Prediction Analytics Using Deep Learning. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-5652-1_6
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