New publication on Algorithmic Fairness and LA-based interventions to reduce “Social Identity Threat“

It took quite some time, but I am happy to announce that a new article, co-authored with Laura Fröhlich, was published (Early Access) in the Journal for Learning Analytics. It is on “Adaptive Interventions Reducing Social Identity Threat to Increase Equity in Higher Distance Education: A Use Case and Ethical Considerations on Algorithmic Fairness”. The article was published as open access and the abstract reads like this:

Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students’ sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and implement an algorithm to classify students as at-risk of experiencing social identity threat. These students would be presented with an intervention fostering a sense of belonging. We systematically analyze the intervention’s intended positive consequences to reduce structural discrimination and increase educational equity, as well as potential risks based on privacy, data protection, and algorithmic fairness considerations. Finally, we provide recommendations for Higher Education Institutions to mitigate risk of bias and unintended consequences during algorithm development and implementation from an ethical perspective.

https://doi.org/10.18608/jla.2024.8301

Froehlich, Laura; Sebastian Weydner-Volkmann (2024): “Adaptive Interventions Reducing Social Identity Threat to Increase Equity in Higher Distance Education: A Use Case and Ethical Considerations on Algorithmic Fairness.” In: Journal of Learning Analytics, Early Access Articles, 1-12. https://doi.org/10.18608/jla.2024.8301

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