Moral machines or tyranny of the majority? A systematic review on predictive bias in education

Lin Li, Lele Sha, Yuheng Li, Mladen Rakovic, Jia Rong, Srecko Joksimovic, Neil Selwyn, Dragan Gasevic, Guanliang Chen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.

Original languageEnglish
Title of host publicationLAK 2023 Conference Proceedings - Towards Trustworthy Learning Analytics - The Thirteenth International Conference on Learning Analytics & Knowledge
EditorsIsabel Hilliger, Hassan Khosravi, Bart Rienties, Shane Dawson
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450398657
Publication statusPublished - 2023
EventInternational Conference on Learning Analytics and Knowledge 2023 - Arlington, United States of America
Duration: 13 Mar 202317 Mar 2023
Conference number: 13th (Proceedings) (Website)


ConferenceInternational Conference on Learning Analytics and Knowledge 2023
Abbreviated titleLAK 2023
Country/TerritoryUnited States of America
Internet address

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