Privacy in LA Research
Understanding the Field to Improve the Practice
Keywords:learning analytics, privacy, definition, scalability, impact, research paper
Protection of student privacy is critical for scaling up the use of learning analytics (LA) in education. Poorly implemented frameworks for privacy protection may negatively impact LA outcomes and undermine trust in the discipline. To design and implement models and tools for privacy protection, we need to understand privacy itself. To develop better understanding and build ground for developing tools and models for privacy protection, this paper examines how privacy hitherto has been defined by LA scholars, and how those definitions relate to the established approaches to define privacy. We conducted a scoping review of 59 articles focused on privacy in LA. In most of these studies (74%), privacy was not defined at all; 6% defined privacy as a right, 11% as a state, 15% as control, and 16% used other approaches to explain privacy in LA. The results suggest a need to define privacy in LA to be able to enact a responsible approach to the use of student data for analysis and decision-making.
Ahn, J., Campos, F., Nguyen, H., Hays, M., & Morrison, J. (2021). Co-designing for privacy, transparency, and trust in K–12 learning analytics. Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK ’21), 12–16 April 2021, Irvine, CA, USA (pp. 55–65). ACM Press. https://doi.org/10.1145/3448139.3448145
Altman, I. (1975). The environment and social behavior: Privacy, personal space, territory, crowding. Brooks/Cole Publishing.
Amo, D., Fonseca, D., Alier, M., García-Peñalvo, F. J., & Casañ, M. J. (2019). Personal data broker instead of blockchain for students’ data privacy assurance. Advances in Intelligent Systems and Computing, 932, 371–380. https://doi.org/10.1007/978-3-030-16187-3_36
Arnold, K. E., & Sclater, N. (2017). Student perceptions of their privacy in leaning analytics applications. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 66–69). ACM Press. https://doi.org/10.1145/3027385.3027392
Bennett, C. J. (1995). The political economy of privacy: Review of the literature. Center for Social and Legal Research.
Berg, A. M., Mol, S. T., Kismihók, G., & Sclater, N. (2016). The role of a reference synthetic data generator within the field of learning analytics. Journal of Learning Analytics, 3(1), 107–128. https://doi.org/10.18608/jla.2016.31.7
Botnevik, S. (2021). Student perceptions of privacy in learning analytics: A quantitative study of Norwegian students. The University of Bergen. https://bora.uib.no/bora-xmlui/handle/11250/2757115
Cerratto Pargman, T., & McGrath, C. (2021). Mapping the ethics of learning analytics in higher education: A systematic literature review of empirical research. Journal of Learning Analytics, 8(2), 123–139. https://learning-analytics.info/index.php/JLA/article/view/7254
Cohen, J. E. (2001). Privacy, ideology, and technology: A response to Jeffrey Rosen. Georgetown Law Faculty Publications and Other Works, vol. 809. https://scholarship.law.georgetown.edu/facpub/809
Cormack, A. (2016). A data protection framework for learning analytics. Journal of Learning Analytics, 3(1 SE), 91–106. https://doi.org/10.18608/jla.2016.31.6
Corrin, L., Kennedy, G., French, S., Shum, S. B., Kitto, K., Pardo, A., West, D., Mirriahi, N., & Colvin, C. (2019). The ethics of learning analytics in Australian higher education. Discussion Paper. https://melbourne-cshe.unimelb.edu.au/__data/assets/pdf_file/0004/3035047/LA_Ethics_Discussion_Paper.pdf
Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue a checklist for trusted learning analytics. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 89–98). ACM Press. https://doi.org/10.1145/2883851.2883893
Drachsler, H., Hoel, T., Scheffel, M., Kismihók, G., Berg, A., Ferguson, R., Chen, W., Cooper, A., & Manderveld, J. (2015). Ethical and privacy issues in the application of learning analytics. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 390–391). ACM Press. https://doi.org/10.1145/2723576.2723642
Ferguson, R. (2019). Ethical challenges for learning analytics. Journal of Learning Analytics, 6(3), 25–30. https://doi.org/10.18608/jla.2019.63.5
Ferguson, R., Hoel, T., Scheffel, M., & Drachsler, H. (2016). Guest editorial: Ethics and privacy in learning analytics. Journal of Learning Analytics, 3(1), 5–15. https://doi.org/10.18608/jla.2016.31.2
Friedman, B., & Hendry, D. (2019). Value sensitive design: Shaping technology with moral imagination. MIT Press. https://doi.org/10.7551/mitpress/7585.001.0001
Gregory, J. (2003). Scandinavian approaches to participatory design. International Journal of Engineering Education, 19(1), 62–74.
Gstrein, S., & Beaulieu, A. (2022). How to protect privacy in a datafied society? A presentation of multiple legal and conceptual approaches. Philosophy and Technology, 35(3). https://doi.org/10.1007/s13347-022-00497-4
Gursoy, M., Inan, A., Nergiz, M., & Saygin, Y. (2017). Privacy-preserving learning analytics: Challenges and techniques. IEEE Transactions on Learning Technologies, 10(1), 68–81. https://doi.org/10.1109/TLT.2016.2607747
Heath, J. (2014). Contemporary privacy theory contributions to learning analytics. Journal of Learning Analytics, 1(1), 140–149. https://doi.org/10.18608/jla.2014.11.8
Hoel, T. (2020). Privacy for learning analytics in the age of big data: Exploring conditions for design of privacy solutions. Doctoral Dissertation. University of Jyväskylä. https://jyx.jyu.fi/handle/123456789/69680
Hoel, T., & Chen, W. (2015). Privacy in learning analytics: Implications for system architecture. In T. Watanabe & K. Seta (Eds.), Theory and Practice for Knowledge Management: Proceedings of the 11th International Conference on Knowledge Management (ICKM 2015), 4–6 November, Osaka, Japan. https://www.semanticscholar.org/paper/Privacy-in-Learning-Analytics-%E2%80%93-Implications-for-Hoel-Chen/46f32e1193d2cbe4a2b049bfa40c28cd252160ef
Hoel, T., & Chen, W. (2016). Privacy-driven design of learning analytics applications: Exploring the design space of solutions for data sharing and interoperability. Journal of Learning Analytics, 3(1), 139–158. https://doi.org/10.18608/jla.2016.31.9
Hoel, T., & Chen, W. (2018). Privacy and data protection in learning analytics should be motivated by an educational maxim: Towards a proposal. Research and Practice in Technology Enhanced Learning, 13(1). https://doi.org/10.1186/s41039-018-0086-8
Hoel, T., & Chen, W. (2019). Privacy engineering for learning analytics in a global market: Defining a point of reference. International Journal of Information and Learning Technology, 36(4), 288–298. https://doi.org/10.1108/IJILT-02-2019-0025
Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938. https://doi.org/10.1007/s11423-016-9477-y
Ifenthaler, D., & Schumacher, C. (2019). Releasing personal information within learning analytics systems. In D. Sampson, J. M. Spector, D. Ifenthaler, P. Isaías, & S. Sergis (Eds.), Learning technologies for transforming large-scale teaching, learning, and assessment (pp. 3–18). Springer. https://doi.org/10.1007/978-3-030-15130-0_1
Jones, K. M. L. (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(24). https://doi.org/10.1186/s41239-019-0155-0
Joksimović, S., Marshall, R., Rakotoarivelo, T., Ladjal, D., Zhan, C., & Pardo, A. (2022). Privacy-driven learning analytics. In E. McKay (Ed.), Manage your own learning analytics. Smart Innovation, Systems and Technologies, vol. 261. Springer. https://doi.org/10.1007/978-3-030-86316-6_1
Jones, K. M. L., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044–1059. https://doi.org/10.1002/asi.24358
Jones, K. M. L., Briney, K. A., Goben, A., Salo, D., Asher, A., & Michael, R. P. (2020). A comprehensive primer to library learning analytics practices, initiatives, and privacy issues. College and Research Libraries, 80(3). https://doi.org/10.5860/crl.81.3.570
Jones, K. M. L., Rubel, A., & LeClere, E. (2020). A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics. Journal of the Association for Information Science and Technology, 71(10), 1227–1241. https://doi.org/10.1002/asi.24327
Jones, K. M. L., & Salo, D. (2018). Learning analytics and the academic library: Professional ethics commitments at a crossroads. College and Research Libraries, 79(3), 304–323. https://doi.org/10.5860/crl.79.3.304
Jones, K. M. L., & VanScoy, A. (2019). The syllabus as a student privacy document in an age of learning analytics. Journal of Documentation, 75(6), 1333–1355. https://doi.org/10.1108/JD-12-2018-0202
Jones, K. M. L., VanScoy, A., Bright, K., & Harding, A. (2021). Do they even care? Measuring instructor value of student privacy in the context of learning analytics. Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54), 5–8 January 2021, Grand Wailea, Maui, HI, USA (pp. 1529–1537). IEEE Computer Society. https://doi.org/10.24251/hicss.2021.185
Khalil, M., & Ebner, M. (2016). De-identification in learning analytics. Journal of Learning Analytics, 3(1). https://doi.org/10.18608/jla.2016.31.8
Kimmons, R. (2021). Safeguarding student privacy in an age of analytics. Educational Technology Research and Development, 69(1), 343–345. https://doi.org/10.1007/s11423-021-09950-1
Kitchin, R. (2021). Data lives: How data are made and shape our world. Bristol University Press.
Krieter, P., Viertel, M., & Breiter, A. (2020). We know what you did last semester: Learners’ perspectives on screen recordings as a long-term data source for learning analytics. In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Eds.), Addressing global challenges and quality education (pp. 187–199). Springer.
Kyritsi, K. H., Zorkadis, V., Stavropoulos, E. C., & Verykios, V. S. (2019). The pursuit of patterns in educational data mining as a threat to student privacy. Journal of Interactive Media in Education, 2(1), 1–10. https://doi.org/10.5334/jime.502
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: Multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
Lawson, C., Beer, C., Rossi, D., Moore, T., & Fleming, J. (2016). Identification of “at risk” students using learning analytics: The ethical dilemmas of intervention strategies in a higher education institution. Educational Technology Research and Development, 64(5), 957–968. https://doi.org/10.1007/s11423-016-9459-0
Li, W., Sun, K., Schaub, F., & Brooks, C. (2021). Disparities in students’ propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education, 32, 564–608. https://doi.org/10.1007/s40593-021-00254-2
Margulis, S. T. (1977). Conceptions of privacy: Current status and next steps. Journal of Social Issues, 33(3), 5–21. https://doi.org/10.1111/j.1540-4560.1977.tb01879.x
Margulis, S. T. (2003). Privacy as a social issue and behavioral concept. Journal of Social Issues, 52(2), 243–261. https://doi.org/10.1111/1540-4560.00063
McHugh, M. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://pubmed.ncbi.nlm.nih.gov/23092060/
Milberg, S., Smith, J., & Burke, S. (2000). Information privacy: Corporate management and national regulation. Organization Science, 11(1), 35–57. https://doi.org/10.1287/orsc.220.127.116.1167
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Munn, Z., Peters, M., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18, 143. https://doi.org/10.1186/s12874-018-0611-x
Mutimukwe, C., Twizeyimana, J. D., & Viberg, O. (2021). Students’ information privacy concerns in learning analytics: Towards model development. Proceedings of the Nordic Learning Analytics Summer Institute, Stockholm. http://ceur-ws.org/Vol-2985/paper3.pdf
Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119–157.
Nissenbaum, H. (2019). Contextual integrity up and down the data food chain. Theoretical Inquiries in Law, 20(1), 221–256. https://doi.org/10.1515/til-2019-0008
O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, 1–13. https://doi.org/10.1177/1609406919899220
Ochoa, X., & Wise, A. F. (2021). Supporting the shift to digital with student-centered learning analytics. Educational Technology Research and Development, 69(1), 357–361. https://doi.org/10.1007/s11423-020-09882-2
Panichas, G. E. (2014). An intrusion theory of privacy. Res Publica, 20, 145–161. Springer. https://doi.org/10.1007/s11158-014-9240-3
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152
Pham, M., Rajic, A., Greig, J., Sargeant, J., & Papadopoulos, A. (2014). A scoping review of scoping reviews: Advancing the approach and enhancing the consistency. Research Synthesis Methods, 5(6), 371–385. https://doi.org/10.1002/jrsm.1123
Potgieter, I. (2020). Privacy concerns in educational data mining and learning analytics. The International Review of Information Ethics, 28, 1–6. https://doi.org/10.29173/irie384
Priedigkeit, M., Weich, A., & Schiering, I. (2021). Learning analytics and privacy: Respecting privacy in digital learning scenarios. In M. Friedewald, S. Schiffner, & S. Krenn (Eds.), Privacy and identity management (pp. 134–150). Springer. https://www.springerprofessional.de/en/learning-analytics-and-privacy-respecting-privacy-in-digital-lea/19025572
Prinsloo, P., & Slade, S. (2015). Student privacy self-management: Implications for learning analytics. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 83–92). ACM Press. https://doi.org/10.1145/2723576.2723585
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1). https://doi.org/10.18608/jla.2016.31.10
Prinsloo, P., & Slade, S. (2018). Mapping responsible learning analytics: A critical proposal. In B. H. Khan, J. R. Corbeil, & M. E. Corbeil (Eds.), Responsible analytics & data mining in education: Global perspectives on quality, support, and decision-making. Routledge.
Prinsloo, P., Slade, S., & Khalil, M. (2022). The answer is (not only) technological: Considering student data privacy in learning analytics. British Journal of Educational Technology, 53(4), 876–893. https://doi.org/10.1111/bjet.13216
Reidenberg, J. R., & Schaub, F. (2018). Achieving big data privacy in education. Theory and Research in Education, 16(3), 263–279. https://doi.org/10.1177/1477878518805308
Richards, N. (2015). Intellectual privacy: Rethinking civil liberties in the digital age. Oxford University Press.
Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student attitudes toward learning analytics in higher education: “The fitbit version of the learning world.” Frontiers in Psychology, 7, 1–11. https://doi.org/10.3389/fpsyg.2016.01959
Romansky, R., & Noninska, I. (2017). An approach for investigation of secure access processes at a combined e-learning environment. AIP Conference Proceedings (Vol. 1910). AIP Publishing. https://doi.org/10.1063/1.5013995
Rosenberg, J. M., & Staudt Willet, K. B. (2021). Balancing privacy and open science in the context of COVID-19: A response to Ifenthaler & Schumacher (2016). Educational Technology Research and Development, 69(1), 347–351. https://doi.org/10.1007/s11423-020-09860-8
Schoeman, F. D. (1984). Philosophical dimensions of privacy: An anthology. Cambridge University Press.
Sclater, N. (2016). Developing a code of practice for learning analytics. Journal of Learning Analytics, 3(1), 16–42. https://doi.org/10.18608/jla.2016.31.3
Seanosky, J., Jacques, D., Kumar, V., & Kinshuk. (2016). Security and privacy in bigdata learning analytics. In V. Vijayakumar & V. Neelanarayanan (Eds.), Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC ’16) (pp. 43–55). Springer.
Silvola, A., Näykki, P., Kaveri, A., & Muukkonen, H. (2021). Expectations for supporting student engagement with learning analytics: An academic path perspective. Computers & Education, 168, 104192. https://doi.org/10.1016/j.compedu.2021.104192
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989–1015. https://doi.org/10.2307/41409970
Solove, D. J. (2006). Taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477–560.
Tobarra, L., Utrilla, A., Robles-Gómez, A., Pastor-Vargas, R., & Hernández, R. (2021). A cloud game-based educative platform architecture: The Cyberscratch project. Applied Sciences, 11(2). https://doi.org/10.3390/app11020807
Tsai, Y., Whitelock-Wainwright, A., & Gašević, D. (2020). The privacy paradox and its implications for learning analytics. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 23–27 March 2020, Frankfurt, Germany (pp. 230–239). ACM Press. https://doi.org/10.1145/3375462.3375536
van Boeijen, A., & Zijlstra, Y. (2020). Culture sensitive design: A guide to culture in practice. BIS Publishers. https://www.bispublishers.com/culture-sensitive-design.html
Wang, Y. (2016). Big opportunities and big concerns of big data in education. TechTrends, 60(4), 381–384. https://doi.org/10.1007/s11528-016-0072-1
Warren, S., & Brandeis, W. (1890). The right to privacy. Harvard Law Review, 4(5), 193–220. https://doi.org/10.2307/1321160
Weinstein, W. L. (1971). The private and the free: A conceptual inquiry. In J. R. Pennock & J. Chapman (Eds.) Privacy, Nomos XIII: Yearbook of the American Society for Political and Legal Philosophy. Atherton Press.
Westin, A. F. (1967). Privacy and freedom. Atheneum.
Whitelock-Wainwright, A., Gašević, D., Tsai, Y.-S., Drachsler, H., Scheffel, M., Munoz-Merino, P., Tammets, K., & Kloos, C. (2020). Assessing the validity of a learning analytics expectation instrument: A multinational study. Journal of Computer Assisted Learning, 26(2), 209–240. https://doi.org/10.1111/jcal.12401
Wright, D., & Raab, C. (2014). Privacy principles, risks and harms. International Review of Law, Computers & Technology, 28(3), 277–298. https://doi.org/10.1080/13600869.2014.913874
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 798–824. https://doi.org/10.17705/1jais.00281
How to Cite
Copyright (c) 2022 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).