Educational Technology Research and Development

, Volume 64, Issue 5, pp 939–955 | Cite as

The ethics of using learning analytics to categorize students on risk

  • Vanessa ScholesEmail author
Development Article


There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat ‘risky’ students differently. This paper analyses this practice using normative theories of discrimination. The analysis suggests the principal ethical concern with the differing treatment is the failure to recognize students as individuals, which may impact on students as agents. This concern is cross-examined drawing on a philosophical argument that suggests there is little or no distinctive difference between assessing individuals on group risk statistics and using more ‘individualized’ evidence. This paper applies this argument to the use of learning analytics to risk-screen students in higher education. The paper offers reasons to conclude that judgment based on group risk statistics does involve a distinctive failure in terms of assessing persons as individuals. However, instructional design offers ways to mitigate this ethical concern with respect to learning analytics. These include designing features into courses that promote greater use of effort-based factors and dynamic rather than static risk factors, and greater use of sets of statistics specific to individuals.


Analytics Ethics Education Screening Discrimination Retention Risk 


Compliance with ethical standards

Conflict of interest

The author declares that she has no conflict of interest.


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Copyright information

© Association for Educational Communications and Technology 2016

Authors and Affiliations

  1. 1.The Open Polytechnic of New ZealandLower HuttNew Zealand

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