The DOI matches its Crossref record; 120 of the 123 references it cites were found in a registry or an index.
Ethical issues in learning analytics: a review of the field
Overview
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 Tzimas & Demetriadis, 2021
Citations
Cites
Self-Regulated Learning and Academic Achievement: An Overview
Zimmerman, B. J. (1990) · Educational Psychologist · doi:10.1207/s15326985ep2501_2 · Cited by 3,687
Journal of Universal Computer Science
Salomaa, A. K., Calude, C. S., & Maurer, H. A. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-000-00-0109 · Cited by 3,403
Instructional Design Theories and Models
(1983) · doi:10.4324/9780203824283 · Cited by 2,192
Penetrating the Fog: Analytics in Learning and Education.
Siemens, G., & Long, P. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1,752
Learning Analytics: The Emergence of a Discipline
Siemens, G. (2013) · American Behavioral Scientist · doi:10.1177/0002764213498851 · Cited by 1,284
Motivational Design for Learning and Performance: The ARCS Model Approach
Keller, J. M. (2010) · doi:10.1007/978-1-4419-1250-3 · Cited by 1,113
Learning Analytics: Ethical Issues and Dilemmas
Slade, S., & Prinsloo, P. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479366 · Cited by 1,078
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.10.002 · Cited by 767
The current landscape of learning analytics in higher education
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.027 · Cited by 766
(2012) · DSpace (Open University in the Netherlands) · Cited by 754
Flagged first, then by citations
3 of the 123 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Cameron, W. B. (1958). Tell me not in mournful numbers. National Education Association of the United States.
- Griffiths, D., Drachsler, H., Kickmeier-Rust, M., Steiner, C., Hoel, T., & Greller, W. (2016). Is privacy a show-stopper for learning analytics? A review of current issues and solutions, (February), 1–30. Retrieved from http://www.laceproject.eu/learning-analytics-review/files/2016/04/LACE-review-6_privacy-show-stopper.pdf
- Kitchenham, B. A. (2004). Procedures for undertaking systematic reviews, Joint technical report. Computer Science Department, Keele University (TR/SE- 0401) and National ICT Australia Ltd. (0400011T.1).
- Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions. Journal of Asynchronous Learning Networks, 16(3), 51–61.
Cited by
Sorted by citations
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Rovshenov, A., & Coşkun, E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5117-9.ch007
Strategies for the Integration of Artificial Intelligence Technology in Higher Education
Al‐Qaimari, G. (2025) · Springer eBooks · doi:10.1007/978-3-032-04788-5_14
Xu, S. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292109
Advancing Global Education Through Learning Analytics
Martins, H. F. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-2622-1.ch004
Fairness-Aware AI in Education: Detecting and Reducing Bias in Student Assessment Systems
Aoudi, S., & Al-Aqrabi, H. (2025) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt69610.2025.11352922
Shingade, S. D., Mudhalwadkar, R. P., & Masal, K. M. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362721
Balancing AI complexity with usability: a study of AI-based UX patterns in business applications
Mahanta, P., Bhattacharya, M., & Habermeier, J. (2026) · Information Technology and People · doi:10.1108/itp-07-2025-1115
Ayasrah, M. N., Al‐Rousan, A. H., Almulla, M. O., & Almulla, A. A. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13941-1 · Cited by 1
From Non-Maleficence to Beneficence: Expanded Ethical Computing in the Era of Large Language Models
Togia, E., Wallace, M., & Liaperdos, J. (2026) · Societies · doi:10.3390/soc16050134
A Systematic Review of Artificial Intelligence in Predicting Undergraduate Degree Completion
Ofusori, L. O., Bokaba, T., & Dogo, E. M. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-16635-7_29
A Feature Selection-based Framework for Course Assessment Optimization
Li, B., Zhu, Y., Zhang, D., & Jia, L. (2026) · International Conference on Big Data and Informatization Education (ICBDIE) · doi:10.1145/3806980.3807120
Ahsan, M., Coura, L., Tahireen, U., Javed, F., Gonçalves, M., Guidoni, D. L., & Delabrida, S. E. (2026) · Journal on Interactive Systems · doi:10.5753/jis.2026.6616
Learning analytics and data-driven decision-making in educational institutions: a policy perspective
Almufti, S. M. (2026) · Journal of Learning and Educational Policy · doi:10.55529/jlep.61.92.102
AI-Driven Digital Analytics for User Behavior and Knowledge Management in Libraries
Syed, K., Dunputh, V. M., Khoso, R. H., & Ortiz, P. J. C. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7262-4.ch007
Huot, S., Loch, S., Hok, C., Phin, K., & Chheang, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7529-8.ch006
Marneri, E., Tzimas, D. E., Karamerou, V., & Vergados, D. J. (2026) · Computers · doi:10.3390/computers15090635
Self-citationTeachers as Custodians of Trust: Linking Learning Analytics and Learning Design in the AI Era
Svetec, B., Divjak, B., Rienties, B., & Muukkonen, H. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39106-3_10
Quantifying Predictive Uncertainty in Student Academic Outcomes Using Conformal PredictionPreprint
Nahin, K. S. A. (2026) · Research Square · doi:10.21203/rs.3.rs-11037494/v1
Metadata
- Authors
- Tzimas, D., & Demetriadis, S.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- Corresponding author
- Tzimas, Dimitrios
- Venue
- Educational Technology Research and Development
- Published
- 2021-03-05
- Volume
- 69
- Issue
- 2
- Pages
- 1101-1133
- ISSN
- 1042-1629 (ISSN-L)
- 1556-6501
- OpenAlex
- W3134656961
- Cited by
- 119
- References
- 123
- Citations by year
- 2026: 20 · 2025: 37 · 2024: 22 · 2023: 26 · 2022: 11 · 2021: 3
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Technology Research and Development (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Education › Digital Education and Society
- Keywords
- learning analytics, ethics of learning analytics, educational data, big data in education, moral norms, code of practice, educational stakeholders, responsible data use, instructional design model
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