Predictive Algorithms in Learning Analytics and their Fairness
Abstract
Predictions in learning analytics are made to improve tailored educational interventions. However, it has been pointed out that machine learning algorithms might discriminate, depending on different measures of fairness. In this paper, we will demonstrate that predictive models, even given a satisfactory level of accuracy, perform differently across student subgroups, especially for different genders or for students with disabilities.
- Citation
- BibTeX
Riazy, S. & Simbeck, K.,
(2019).
Predictive Algorithms in Learning Analytics and their Fairness.
In:
Pinkwart, N. & Konert, J.
(Hrsg.),
DELFI 2019.
Bonn:
Gesellschaft für Informatik e.V..
(S. 223-228).
DOI: 10.18420/delfi2019_305
@inproceedings{mci/Riazy2019,
author = {Riazy, Shirin AND Simbeck, Katharina},
title = {Predictive Algorithms in Learning Analytics and their Fairness},
booktitle = {DELFI 2019},
year = {2019},
editor = {Pinkwart, Niels AND Konert, Johannes} ,
pages = { 223-228 } ,
doi = { 10.18420/delfi2019_305 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
author = {Riazy, Shirin AND Simbeck, Katharina},
title = {Predictive Algorithms in Learning Analytics and their Fairness},
booktitle = {DELFI 2019},
year = {2019},
editor = {Pinkwart, Niels AND Konert, Johannes} ,
pages = { 223-228 } ,
doi = { 10.18420/delfi2019_305 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
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More Info
ISBN: 978-3-88579-691-6
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2019
Language:
(en)

Content Type: Text/Conference Paper