Towards a Secure and Privacy Compliant Framework for Educational Data Mining
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-33080-3_35
Reference21 articles.
1. Amo, D., Torres, R., Canaleta, X., Herrero-Martín, J., Rodríguez-Merino, C., Fonseca, D.: Seven principles to foster privacy and security in educational tools: Local Educational Data Analytics. In: ACM International Conference Proceeding Series, pp. 730–737 (2020)
2. UNESCO: minding the data: protecting learners’ privacy and security. UNESCO (2022). https://unesdoc.unesco.org/ark:/48223/pf0000381494
3. Vidakis, N., Barianos, A., Trampas, A., Papadakis, S., Kalogiannakis, M., Vassilakis, K.: In-game raw data collection and visualization in the context of the “ThimelEdu” educational game. Commun. Comput. Inf. Sci. 1220, 629–646 (2020)
4. Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Exp. Syst. Appl. 41(4, Part 1), 1432–1462 (2014), https://www.sciencedirect.com/science/article/pii/S0957417413006635
5. Pardo, A., Siemens, G.: Ethical and privacy principles for learning analytics: Ethical and privacy principles. Br. J. Educ. Technol. 45(3), 438–450 (2014), https://onlinelibrary.wiley.com/doi/10.1111/bjet.12152
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