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A Data Pipeline to Preserve Privacy in Educational Settings

Published:22 February 2022Publication History

ABSTRACT

In the sensitive field of distance learning data handling should lead to actionable knowledge and, at the same time, ought to respect the privacy of the students. The hype of online learning led to a plethora of data but also raised ethical issues regarding privacy protection which is mainly addressed in the GDPR. There is an optimum equilibrium between making out the most of available data and protecting the individual freedom of the participants. In this paper, we propose a data pipeline that could be incorporated in a Learning Analytics cycle and provide anonymous, low-risk data.

References

  1. Kum, Hye-Chung, Krishnamurthy, A., Machanavajjhala, A., & Ahalt, S. C. 2013. Social genome: Putting big data to work for population informatics. Computer, 47(1), 56-63.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Reich, Justin, and José A. Ruipérez-Valiente. 2019. The MOOC pivot. Science, 363(6423), 130-131.Google ScholarGoogle ScholarCross RefCross Ref
  3. Martin A. Fischler and Robert C. Bolles. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395. https://doi.org/10.1145/358669.358692Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bozkurt, Aras, Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., & Paskevicius, M. 2020. A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1-126.Google ScholarGoogle Scholar
  5. Verykios, S. Vassilios, Bertino, E., Fovino, I. N., Provenza, L. P., Saygin, Y., & Theodoridis, Y. 2004. State-of-the-art in privacy preserving data mining. ACM Sigmod Record, 33(1), 50-57Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Vatsalan, D., Christen, P., & Verykios, V. S. 2013. A taxonomy of privacy-preserving record linkage techniques. Information Systems, 38(6), 946-969.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kyritsi, Kyriaki, Zorkadis, Vasilios., Stavropoulos, Elias, & Verykios, S. Vassilios. 2018. Privacy Issues in Learning Analytics. Blended and Online Learning, 218.Google ScholarGoogle Scholar
  8. Jones, Kyle ML. 2019. Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16 vol. 1, 1-22.Google ScholarGoogle Scholar
  9. Kyritsi, Kyriaki, Zorkadis, Vasilios, Stavropoulos, Elias, & Verykios, S. Vassilios. 2019. The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy. Journal of Interactive Media in Education.Google ScholarGoogle ScholarCross RefCross Ref
  10. Chicaiza, Janneth Cabrera-Loayza Ma. Carmen, Elizalde, Rene, Piedra, Nelson 2020. Application of data anonymization in Learning Analytics. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems. 1-6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Pardo, Abelardo and Siemens, George. 2014. Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.Google ScholarGoogle ScholarCross RefCross Ref
  12. Prinsloo, Paul & Slade, Sharon. 2017. Ethics and learning analytics: Charting the (un) charted. SoLAR.Google ScholarGoogle Scholar
  13. Kitto, Kirsty and Simon Knight. 2019. Practical ethics for building learning analytics. British Journal of Educational Technology, 50(6), 2855-2870.Google ScholarGoogle ScholarCross RefCross Ref
  14. Jones, Kyle ML. 2019. " Just Because You Can Doesn't Mean You Should": Practitioner Perceptions of Learning Analytics Ethics. portal: Libraries and the Academy, 19(3), 407-428.Google ScholarGoogle ScholarCross RefCross Ref
  15. Slade, Sharon, and Alan Tait. 2019. Global guidelines: Ethics in learning analytics.Google ScholarGoogle Scholar
  16. Slade, Sharon and Prinsloo, Paul. 2013. Learning analytics: ethical issues and dilemmas. American Behavioral Scientist, 57 vol. 10, 1509–1528.Google ScholarGoogle Scholar
  17. Voigt, Paul, and Axel Von dem Bussche. 2017. The EU general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 10, 3152676.Google ScholarGoogle Scholar
  18. Hoel, Tore, and Weiqin Chen. 2016. Implications of the European data protection regulations for learning analytics design. In Workshop paper presented at the international workshop on learning analytics and educational data mining (LAEDM 2016) in conjunction with the international conference on collaboration technologies (CollabTech 2016), Kanazawa, Japan-September. 14-16.Google ScholarGoogle Scholar
  19. Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kotter, Thorsten Meinl, Peter Ohl, Kilian Thiel and Bernd Wiswedel. 2009. KNIME-the Konstanz information miner: version 2.0 and beyond. AcM SIGKDD explorations Newsletter, 11(1), 26-31.Google ScholarGoogle Scholar
  20. Garfinkel, Simson L. 2015. De-identification of personal information. National institute of standards and technology.Google ScholarGoogle Scholar

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            cover image ACM Other conferences
            PCI '21: Proceedings of the 25th Pan-Hellenic Conference on Informatics
            November 2021
            499 pages

            Copyright © 2021 ACM

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            New York, NY, United States

            Publication History

            • Published: 22 February 2022

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            Overall Acceptance Rate190of390submissions,49%

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