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The ethics of using learning analytics to categorize students on risk

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Abstract

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.

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Authors and Affiliations

  1. The Open Polytechnic of New Zealand, 3 Cleary St, Waterloo, Lower Hutt, 5040, New Zealand

    Vanessa Scholes

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  1. Vanessa Scholes
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Correspondence to Vanessa Scholes.

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Scholes, V. The ethics of using learning analytics to categorize students on risk. Education Tech Research Dev 64, 939–955 (2016). https://doi.org/10.1007/s11423-016-9458-1

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