Security and privacy in academic data management at schools: SPADATAS Project

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Abstract

The introduction of cloud technology in educational settings over the past ten years has enabled organizations to embrace a data-driven decision-making paradigm. Schools and colleges are undergoing rapid digital updating procedures due to the use of outside technological solutions in the cloud, affecting how students learn and are taught. Regarding data, teaching and learning processes are improved by technology that gathers and analyzes student data to present useful information. With this technological shift comes the pervasiveness of data thanks to cloud storage. This means that in many instances, outside the purview of schools and universities, certain actors may gather, manage, and treat educational data on private servers and data centers. This privatization allows data leaks, record manipulation, and unwanted access. To help primary and secondary schools understand what data-driven decision-making entails for an educational institution and what problems with data fragility are related to current educational technology and data academic management, the current paper outlines the main goals of the SPADATAS project, its organizational structure, and its key issues. It also offers tools and frameworks to safeguard the privacy, security, and confidentiality of students’ data.

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Academic data, Privacy, Security, Management, Confidentially of student’s data

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Amo-Filva, D., Escudero, D. F., Sanchez-Sepulveda, M. V., García-Holgado, A., García-Holgado, L., García-Peñalvo, F. J., Orehovački, T., Krašna, M., Pesek, I., Marchetti, E., Valente, A., Witfelt, C., Ružić, I., Fraoua, K. E., & Moreira, F. (2023). Security and privacy in academic data management at schools: SPADATAS Project. In P. Zaphiris, & A. Ioannou (Eds.), [Proceedings of] Learning and Collaboration Technologies 10th International Conference, LCT 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Part I (Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14040), Copenhagen, Denmark, 23-28 july 2023, (pp. 3-16). Springer. https://doi.org/10.1007/978-3-031-34411-4_1. Repositório Institucional UPT. http://hdl.handle.net/11328/4927

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978-3-031-34411-4
978-3-031-34410-7

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