Towards More Transparency in Learning Analytics

Sharing Information with University Students Increases their Awareness of Data Collection Practices

Authors

DOI:

https://doi.org/10.18608/jla.2025.8713

Keywords:

analytics practice, transparency, ethics, privacy, disclosure, research paper

Abstract

As learning analytics practices become more commonplace in educational settings, student knowledge about the collection and use of their data becomes more of an interest. How students perceive the collection and use of their data has been researched for many years, with legitimate privacy and ethical concerns raised. While various guidelines, models, and frameworks have been proposed to address these concerns, the ways educational institutions practically address them by providing more information for increased transparency has yet to be widely investigated. The present study provides an initial investigation into the effectiveness of three different formats of data disclosure statements in a higher education setting. Participants were presented with one of three different formats from a fictional university: 1) a generic text, 2) a detailed text, or 3) an icon-based “nutrition label.” Participants then completed a survey to assess their understanding and perceptions of data collection practices. Results suggest that regardless of format, participants demonstrated an increased understanding of these practices when the disclosure was not generic. Additionally, student acceptance of data collection and beliefs about sharing were unaffected by disclosure of any kind. This study provides initial evidence to inform learning analytics practices to address identified concerns from students around a lack of transparency. Universities and other institutions in the higher education sector may revisit their data disclosure methods and language to ensure that they are both accurate and transparent, so that students better understand data collection within the scope of their studies.

References

Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2). https://doi.org/10.24059/olj.v20i2.790

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics. EDUCAUSE Review, 42(4), 40–57. https://er.educause.edu/-/media/files/article-downloads/erm0742.pdf

de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https://doi.org/10.1111/bjet.12212

Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue: A checklist for trusted learning analytics. In LAK ’16: Proceedings of the sixth international conference on learning analytics & knowledge (pp. 89–98). ACM Press. https://doi.org/10.1145/2883851.2883893

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/bf03193146

Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317. https://doi.org/10.1504/ijtel.2012.051816

Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15(4), 313–331. https://doi.org/10.1016/j.learninstruc.2005.07.001

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

Held, M. B. E. (2019). Decolonizing research paradigms in the context of settler colonialism: An unsettling, mutual, and collaborative effort. International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406918821574

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), Article 20. https://doi.org/10.1186/s41039-018-0086-8

Hoel, T., Griffiths, D., & Chen, W. (2017). The influence of data protection and privacy frameworks on the design of learning analytics systems. In LAK ’17: Proceedings of the seventh international learning analytics & knowledge conference (pp. 243–252). ACM Press. https://doi.org/10.1145/3027385.3027414

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

Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47. https://doi.org/10.18608/jla.2014.11.3

Joksimović, S., Marshall, R., Rakotoarivelo, T., Ladjal, D., Zhan, C., & Pardo, A. (2021). Privacy-driven learning analytics. In E. McKay (Ed.), Manage your own learning analytics: Implement a Rasch modelling approach (pp. 1–22). Springer Cham. https://doi.org/10.1007/978-3-030-86316-6_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(1), Article 24. https://doi.org/10.1186/s41239-019-0155-0

Kew, S. N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405–427. https://doi.org/10.1007/s10758-021-09541-2

Land, R., & Bayne, S. (2005). Screen or monitor? Issues of surveillance and disciplinary power in online learning environments. In R. Land & S. Bayne (Eds.), Education in cyberspace (pp. 164–177). Routledge. https://doi.org/10.4324/9780203391068-21

Liu, Q., & Khalil, M. (2023). Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology, 54(6), 1715–1747. https://doi.org/10.1111/bjet.13388

Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. https://doi.org/10.1177/0002764213479367

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. https://er.educause.edu/-/media/files/article-downloads/erm1151.pdf

Moore, P., & Fitz, C. (1993). Gestalt theory and instructional design. Journal of Technical Writing and Communication, 23(2), 137–157. https://doi.org/10.2190/g748-by68-l83t-x02j

Mutimukwe, C., Viberg, O., Oberg, L.-M., & Cerratto‐Pargman, T. (2022). Students’ privacy concerns in learning analytics: Model development. British Journal of Educational Technology, 53(4), 932–951. https://doi.org/10.1111/bjet.13234

Nichols, M. (2024). Development of an approved learning analytics ethics position. Open Learning: The Journal of Open, Distance and e-Learning, 39(3), 212–225. https://doi.org/10.1080/02680513.2021.1986376

Nistor, N., & Hernández-García, Á. (2018). What types of data are used in learning analytics? An overview of six cases. Computers in Human Behavior, 89, 335–338. https://doi.org/10.1016/j.chb.2018.07.038

Norz, L.-M., Dornauer, V., Hackl, W. O., & Ammenwerth, E. (2023). Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis. The Internet and Higher Education, 56, Article 100894. https://doi.org/10.1016/j.iheduc.2022.100894

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

Prinsloo, P., & Slade, S. (2018). Student consent in learning analytics: The devil in the details? In J. Lester, C. Klein, A. Johri, & H. Rangwala (Eds.), Learning analytics in higher education (pp. 118–139). Routledge. https://doi.org/10.4324/9780203731864

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

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, Article 1959. https://doi.org/10.3389/fpsyg.2016.01959

Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143–159. https://doi.org/10.1080/01972243.2016.1130502

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366

Slade, S., Prinsloo, P., & Khalil, M. (2019). Learning analytics at the intersections of student trust, disclosure and benefit. In LAK19: Proceedings of the 9th international conference on learning analytics & knowledge (pp. 235–244). ACM Press. https://doi.org/10.1145/3303772.3303796

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

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.

Tsai, Y.-S., Whitelock-Wainwright, A., & Gašević, D. (2020). The privacy paradox and its implications for learning analytics. In LAK ’20: Proceedings of the tenth international conference on learning analytics & knowledge (pp. 230–239). ACM Press. https://doi.org/10.1145/3375462.3375536

Tsai, Y.-S., Whitelock-Wainwright, A., & Gašević, D. (2021). More than figures on your laptop: (Dis)trustful implementation of learning analytics. Journal of Learning Analytics, 8(3), 81–100. https://doi.org/10.18608/jla.2021.7379

Tuck, E., & Yang, K. W. (2014). R-words: Refusing research. In D. Paris & M. T. Winn (Eds.), Humanizing research: Decolonizing qualitative inquiry with youth and communities (pp. 223–248). SAGE Publications. https://doi.org/10.4135/9781544329611.n12

Viberg, O., Engström, L., Saqr, M., & Hrastinski, S. (2022). Exploring students’ expectations of learning analytics: A person-centered approach. Education and Information Technologies, 27(6), 8561–8581. https://doi.org/10.1007/s10639-022-10980-2

Wang, F., Li, W., Mayer, R. E., & Liu, H. (2018). Animated pedagogical agents as aids in multimedia learning: Effects on eye-fixations during learning and learning outcomes. Journal of Educational Psychology, 110(2), 250–268. https://doi.org/10.1037/edu0000221

Whitman, M. (2021). Modeling ethics: Approaches to data creep in higher education. Science and Engineering Ethics, 27(6), Article 71. https://doi.org/10.1007/s11948-021-00346-1

Worsley, M., & Ochoa, X. (2020). Towards collaboration literacy development through multimodal learning analytics. In Companion proceedings of the tenth international conference on learning analytics & knowledge (LAK20) (pp. 585–595). Society for Learning Analytics Research.

Downloads

Published

2025-11-24

How to Cite

Sepp, S. (2025). Towards More Transparency in Learning Analytics: Sharing Information with University Students Increases their Awareness of Data Collection Practices. Journal of Learning Analytics, 12(3), 34-46. https://doi.org/10.18608/jla.2025.8713

Issue

Section

Research Papers