Ethical and privacy principles for learning analytics
Abstract
The massive adoption of technology in learning processes comes with an equally large capacity to track learners. Learning analytics aims at using the collected information to understand and improve the quality of a learning experience. The privacy and ethical issues that emerge in this context are tightly interconnected with other aspects such as trust, accountability and transparency. In this paper, a set of principles is identified to narrow the scope of the discussion and point to pragmatic approaches to help design and research learning experiences where important ethical and privacy issues are considered.
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Chilling brought about by learning analytics can harm the sharing of viewpoints and decrease pushing intellectual boundaries, both of which often require learner communities to engage in free, unrestricted speech and inquiry.
“Chilling brought about by LA can harm the sharing of viewpoints and decrease pushing intellectual boundaries, both of which often require learner communities to engage in free, unrestricted speech and inquiry.98”
From Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads (2018) · cites this paper as [98]
The use of AI and learning analytics in education entails a massive amount of personal data generated, captured, and analyzed to optimize learning experiences.
“This digital revolution in education, particularly the use of AI and learning analytics in the field of education, entails a massive amount of personal data generated, captured, and analyzed to optimize learning experiences (Tzimas & Demetriadis, 2021; Pardo & Siemens, 2014).”
From Ethical principles for artificial intelligence in education (2023) · cites this paper as Pardo & Siemens, 2014
In learning analytics, privacy and ethics are intertwined with trust, accountability, and transparency rather than confined to data security.
“In learning analytics, privacy and ethics are intertwined with trust, accountability, and transparency rather than confined to data security (Pardo & Siemens, 2014).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as Pardo & Siemens, 2014
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“First, the range and types of data collected have led to concerns about intrusions into spaces generally deemed private or personal [28].”
BackgroundInscribing ethics and values in designs for learning: a problematic
Gray, C. M., & Boling, E. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9478-x · Cited by 86 · see citing works
Lawson, C., Beer, C., Rossi, D. M., Moore, T., & Fleming, J. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9459-0 · Cited by 86 · see citing works
Informing learning design through analytics: Applying network graph analysis
Ifenthaler, D., Gibson, D., & Dobozy, E. (2018) · Australasian Journal of Educational Technology · doi:10.14742/ajet.3767 · Cited by 85 · see citing works
“Learning analytics should be aligned with organisational principles and values, include all stakeholders, collect, use, and analyse data transparently and free of bias, and be beneficial for all involved stakeholders (Ifenthaler & Tracey, 2016; Pardo & Siemens, 2014).”
BackgroundLearning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads
Jones, K., & Salo, D. (2018) · College & Research Libraries · doi:10.5860/crl.79.3.304 · Cited by 85 · see citing works
Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics
Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018) · Technology, Knowledge and Learning · doi:10.1007/s10758-017-9329-9 · Cited by 84 · see citing works
“However, the issue of whether and how to obtain consent from students for the use of their data in learning analytics is frequently raised (e.g., Greller and Drachsler 2012; Pardo and Siemens 2014; Roberts et al. 2016a; Slade and Prinsloo 2013).”
BackgroundMonitoring, awareness and reflection in blended technology enhanced learning: a systematic review
Rodríguez‐Triana, M. J., Prieto, L. P., Vozniuk, A., Boroujeni, M. S., Schwendimann, B. A., Holzer, A., & Gillet, D. (2017) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2017.084489 · Cited by 89 · see citing works
“…include fragmentation of the data (since records about the students are often located in multiple institutional databases with different stakeholders) and privacy or ethical concerns emerging when working with such personally identifiable information (Pardo & Siemens, 2014; Drachsler et al., 2015).”
BackgroundUnderstanding privacy and data protection issues in learning analytics using a systematic review
Liu, Q., & Khalil, M. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13388 · Cited by 113 · see citing works
The ethics of using learning analytics to categorize students on risk
Scholes, V. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9458-1 · Cited by 79 · see citing works
“…been the subject of increasing discussion (see, e.g., Campbell et al. 2007; Simpson 2009; Kay et al. 2012; Slade and Prinsloo 2013; Johnson 2014; Pardo and Siemens 2014; Polonetsky and Tene 2014; Prinsloo and Slade 2014; Sclater 2014a; Willis 2014; Willis and Pistilli 2014; Sclater and Bailey…”
BackgroundInfluentialLearning analytics beyond the LMS
Kitto, K., Cross, S., Waters, Z., & Lupton, M. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723627 · Cited by 78 · see citing works
“The experimental cMOOC idea pioneered in 2008 by Siemens and Downes [9] demonstrated that social media services can be harnessed to help achieve CL, with different tools combined by individual students to form their own personal learning environments (PLE) and personal learning networks (PLN) [6].”
BackgroundInfluentialIt's My Data! Tensions Among Stakeholders of a Learning Analytics Dashboard
Sun, K., Mhaidli, A. H., Watel, S., Brooks, C. A., & Schaub, F. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300824 · Cited by 77 · see citing works
“…the higher education experience, many issues and challenges arise, including ethical and transparent use of student data [26], student data privacy concerns and rights [14, 41], considerations of informed consent [21], data access control [47], and responsible interpretation of student data [23].”
BackgroundScalability, Sustainability, and Ethicality of Multimodal Learning Analytics
Yan, L., Zhao, L., Gasevic, D., & Martinez-Maldonado, R. (2022) · LAK22: 12th International Learning Analytics and Knowledge Conference · doi:10.1145/3506860.3506862 · Cited by 83 · see citing works
“The ethicality of LA systems can be defined as the systematisa-tion of appropriate and inappropriate behaviour in physical spaces, as determined by all stakeholders [52].”
BackgroundPutting an ethical lens on learning analytics
West, D., Huijser, H., & Heath, D. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9464-3 · Cited by 77 · see citing works
“Clarity around this value base is important at every point in the learning analytics journey but particularly critical in the early stages of its implementation (Pardo and Siemens 2014).”
BackgroundInfluentialMultimodal Learning Analytics: Rationale, Process, Examples, and Direction
Ochoa, X. (2022) · The Handbook of Learning Analytics · doi:10.18608/hla22.006 · Cited by 81 · see citing works
Ifenthaler, D., & Tracey, M. W. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9480-3 · Cited by 74 · see citing works
Jones, K. M. L., Rubel, A., & LeClere, E. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24327 · Cited by 73 · see citing works
“Like privacy issues associated with data mining in other contexts, LA also raise questions regarding individual autonomy and informational controls (Pardo & Siemens, 2014; Rubel & Jones, 2017) in ways that bring to the fore power, fairness, and transparency concerns (Lawson, Beer, Rossi, Moore, &…”
BackgroundThe potential for student performance prediction in small cohorts with minimal available attributes
Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12836 · Cited by 76 · see citing works
“These are well documented in a number of research papers including Pardo and Siemens (2014) and de Freitas et al. (2015).”
BackgroundEr, E., Dimitriadis, Y. A., & Gašević, D. (2020) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2020.1764490 · Cited by 72 · see citing works
“These issues are addressed by applying the principles of learning analytics deployment proposed by Pardo and Siemens (2014), which are transparency, student control over data, right of access, and accountability and assessment.”
MethodUsing trace data to enhance Students' self-regulation: A learning analytics perspective
Ye, D., & Pennisi, S. (2022) · The Internet and Higher Education · doi:10.1016/j.iheduc.2022.100855 · Cited by 69 · see citing works
Lang, C., Wise, A. F., Merceron, A., Gašević, D., & Siemens, G. (2022) · Solar eBooks · doi:10.18608/hla22.001 · Cited by 68 · see citing works
Self-citationArtificial intelligence in schools: Towards a democratic future
Gray, S. L. (2020) · London Review of Education · doi:10.14324/lre.18.2.02 · Cited by 65 · see citing works
“The scaling up has associated impacts on privacy (Har Carmel, 2016; Pardo and Siemens, 2014; Selwyn, 2015; Tudor, 2015; Williamson, 2017a).”
BackgroundApplications of Learning Analytics in High Schools: A Systematic Literature Review
de Sousa, E. B. G., Rosa, B. A. B., Mello, R. F., Falcão, T. P., Vesin, B., & Gašević, D. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.737891 · Cited by 65 · see citing works
“Ethical concerns : In a similar direction, it is important to consider ethical issues (Pardo and Siemens, 2014).”
BackgroundA Data Protection Framework for Learning Analytics
Cormack, A. N. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.6 · Cited by 64 · see citing works
“Like them it seeks to “understand and optimize learning and the environment in which it occurs” (Pardo & Siemens, 2014, p.443).”
BackgroundMethodInfluentialBoscardin, C., Fergus, K. B., Hellevig, B., & Hauer, K. E. (2017) · Medical Teacher · doi:10.1080/0142159x.2017.1396306 · Cited by 64 · see citing works
“(Slade and Prinsloo 2013; Pardo and Siemens 2014).”
Privacy and E-Learning: A Pending Task
Alier, M., Guerrero, M. J. C., Amo, D., Severance, C., & Fonseca, D. (2021) · Sustainability · doi:10.3390/su13169206 · Cited by 63 · see citing works
“Pardo and Siemens define ethics as “the systematization of correct and incorrect behavior in virtual spaces according to all stakeholders” [41].”
BackgroundThe Student Expectations of Learning Analytics Questionnaire
Whitelock‐Wainwright, A., Gašević, D., Tejeiro, R., Tsai, Y., & Bennett, K. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12366 · Cited by 63 · see citing works
How was the activity? A visualization support for a case of location‐based learning design
Melero, J., Hernández‐Leo, D., Sun, J., Santos, P., & Blat, J. (2014) · British Journal of Educational Technology · doi:10.1111/bjet.12238 · Cited by 62 · see citing works
“Using learning analytics as strategies to measure, collect, analyse and report data about learners and their contexts, seem to be the appropriate approach to base the features behind those tools (Pardo & Siemens, 2014; Ellis, 2013).”
MethodIntegrating AI and machine learning in STEM education: Challenges and opportunities
Joseph, O. B., & Uzondu, N. C. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i8.1379 · Cited by 64 · see citing works
Objectives, methodologies and research issues of learning analytics
Hwang, G., Chu, H., & Yin, C. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2017.1287338 · Cited by 60 · see citing works
“In the meantime, several scholars have started to emphasize the issues related to privacy and security management for those open educational data (Pardo & Siemens, 2014; Slade & Prinsloo, 2013).”
BackgroundDisparities in Students’ Propensity to Consent to Learning Analytics
Li, W., Sun, K., Schaub, F., & Brooks, C. A. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00254-2 · Cited by 70 · see citing works
Leveraging machine learning models in evaluating ADMET properties for drug discovery and development
Magesh, V., Rao, G. C., Karthik, M. J., & Maddi, S. (2025) · ADMET & DMPK · doi:10.5599/admet.2772 · Cited by 66 · see citing works
Student perceptions of their privacy in leaning analytics applications
Arnold, K. E., & Sclater, N. (2017) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3027385.3027392 · Cited by 56 · see citing works
“[6, 7] Ifenthaler and Schumacher claim that students would be willing to share more extensive data if, in return, the learning analytics system “provided rich and meaningful information.””
BackgroundMethodThe PERLA Framework: Blending Personalization and Learning Analytics
Chatti, M. A., & Muslim, A. (2019) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v20i1.3936 · Cited by 56 · see citing works
“…which analytics methods are applied to the data, how long is the data valid and available, the purposes for which the data will be used, under which conditions, and which measures are undertaken to preserve and protect the identity of the learner (Pardo & Siemens, 2014; Slade & Prinsloo, 2013).”
MethodBhimdiwala, A., Neri, R. C., & Gomez, L. M. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00278-8 · Cited by 64 · see citing works
“(2) Teachers find the technology a true representation of and complement to their teaching identity (Pardo & Siemens, 2014; Tsai & Gašević, 2017).”
BackgroundTsai, Y., Moreno-Marcos, P. M., Tammets, K., Kollom, K., & Gašević, D. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170367 · Cited by 55 · see citing works
“The wide range and types of data collected could induce discomfort among data subjects due to a sense of surveillance, leading to resistance to LA [19].”
BackgroundMangaroska, K., Martínez-Maldonado, R., Vesin, B., & Gašević, D. (2021) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12542 · Cited by 52 · see citing works
“…of reference comes from the existing literature in LA and MLA (Azevedo, 2015; Beattie et al., 2014; Blikstein & Worsley, 2016; Järvelä et al., 2019; Pardo & Siemens, 2014; Slade & Prinsloo, 2013), and the forthcoming challenges of technological evolution addressed by Stephanidis et al. (2019).”
BackgroundAgasisti, T., & Bowers, A. J. (2017) · Handbook of Contemporary Education Economics · doi:10.4337/9781785369070.00014 · Cited by 83 · see citing works
Learning Analytics in Education: Literature Review and Case Examples From Vocational Education
Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2019) · Scandinavian Journal of Educational Research · doi:10.1080/00313831.2019.1649718 · Cited by 52 · see citing works
“Many scientists have proposed and discussed ideas on how this should be done (Drachsler & Greller, 2016; Pardo & Siemens, 2014; Rodríguez-Triana, Martínez-Monés, & Villagrá-Sobrino, 2016; Sclater, 2016; Slade & Prinsloo, 2013).”
BackgroundAlwahaby, H., Cukurova, M., Papamitsiou, Z. K., & Giannakos, M. N. (2022) · Springer eBooks · doi:10.1007/978-3-031-08076-0_12 · Cited by 67 · see citing works
Implementing next generation privacy and ethics research in education technology
Marshall, R., Pardo, A., Smith, D., & Watson, T. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13224 · Cited by 50 · see citing works
Self-citationThey shall be fair, transparent, and robust: auditing learning analytics systems
Simbeck, K. (2023) · AI and Ethics · doi:10.1007/s43681-023-00292-7 · Cited by 58 · see citing works
“Fairness, transparency, and explainability are inevitable dimensions in LA, especially as learners' professional futures are at stake [35, 77, 86, 97].”
InfluentialArtificial Intelligence and Teachers’ New Ethical Obligations
Adams, C., Pente, P., Lemermeyer, G., Turville, J., & Rockwell, G. (2022) · The International Review of Information Ethics · doi:10.29173/irie483 · Cited by 49 · see citing works
Jones, K. M. L. (2019) · portal Libraries and the Academy · doi:10.1353/pla.2019.0025 · Cited by 48 · see citing works
Alvarez, C. P., Martínez‐Maldonado, R., & Shum, S. B. (2020) · International Conference on Learning Analytics & Knowledge (LAK) · doi:10.1145/3375462.3375476 · Cited by 47 · see citing works
Daoudi, I. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11087-4 · Cited by 54 · see citing works
“(Pardo & Siemens, 2014) have proposed also that it is indispensable to develop legislation meth-ods for data collection in order to avoid the problem of students’ privacy violation.”
BackgroundHoel, T., & Chen, W. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.9 · Cited by 47 · see citing works
De-Identification in Learning Analytics
Khalil, M., & Ebner, M. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.8 · Cited by 78 · see citing works
“De-identification techniques have been reviewed as a right of access principle in learning analytics deployment (Pardo & Siemens, 2014).”
MethodTeacher and Student Facing Learning Analytics
van Leeuwen, A., Teasley, S. D., & Wise, A. F. (2022) · Solar eBooks · doi:10.18608/hla22.013 · Cited by 47 · see citing works
Blended learning in higher education: the integrated and distributed model and a thematic analysis
Starčič, A. I. (2024) · Discover Education · doi:10.1007/s44217-024-00239-y · Cited by 48 · see citing works
Farah, J. C., Vozniuk, A., Rodríguez‐Triana, M. J., et al. (2018) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2018.00059 · Cited by 45 · see citing works
“The rise of digital education and learning analytics (LA) has led to a significant increase in the amount of information collected during educational activities [1].”
Background
- Authors
- Pardo, A., & Siemens, G.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Pardo, Abelardo
- Siemens, George
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2014-04
- Volume
- 45
- Issue
- 3
- Pages
- 438-450
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W2149282659
- Semantic Scholar
- 649ba38b9524f91b731e72ddde10075a45f21993
- Cited by
- 577
- References
- 40
- Citations by year
- 2026: 88 · 2025: 34 · 2024: 55 · 2023: 61 · 2022: 59 · 2021: 58 · 2020: 66 · 2019: 40 · 2018: 31 · 2017: 30 · 2016: 39 · 2015: 11 · 2014: 5
- FWCI
- 42.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- British Journal of Educational Technology (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Keywords
- learning analytics, privacy, ethics, student progress monitoring, trust, accountability, transparency, ethical principles, privacy principles, educational technology
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