Details
| Original language | English |
|---|---|
| Title of host publication | Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
| Subtitle of host publication | International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I |
| Editors | Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana, Konstantinos Sechidis, Arif Canakoglu, Sara Pido, Pietro Pinoli, Albert Bifet, Sepideh Pashami |
| Place of Publication | Cham |
| Publisher | Springer Nature |
| Pages | 119-136 |
| Number of pages | 18 |
| ISBN (electronic) | 978-3-031-23618-1 |
| ISBN (print) | 978-3-031-23617-4 |
| Publication status | Published - 31 Jan 2023 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 1752 CCIS |
| ISSN (Print) | 1865-0929 |
| ISSN (electronic) | 1865-0937 |
Abstract
Keywords
- cs.LG, cs.CY, Educational data mining, Fairness measures, Machine learning, Student performance prediction, Fairness
ASJC Scopus subject areas
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Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I. ed. / Irena Koprinska; Paolo Mignone; Riccardo Guidotti; Szymon Jaroszewicz; Holger Fröning; Francesco Gullo; Pedro M. Ferreira; Damian Roqueiro; Gaia Ceddia; Slawomir Nowaczyk; João Gama; Rita Ribeiro; Ricard Gavaldà; Elio Masciari; Zbigniew Ras; Ettore Ritacco; Francesca Naretto; Andreas Theissler; Przemyslaw Biecek; Wouter Verbeke; Gregor Schiele; Franz Pernkopf; Michaela Blott; Ilaria Bordino; Ivan Luciano Danesi; Giovanni Ponti; Lorenzo Severini; Annalisa Appice; Giuseppina Andresini; Ibéria Medeiros; Guilherme Graça; Lee Cooper; Naghmeh Ghazaleh; Jonas Richiardi; Diego Saldana; Konstantinos Sechidis; Arif Canakoglu; Sara Pido; Pietro Pinoli; Albert Bifet; Sepideh Pashami. Cham: Springer Nature, 2023. p. 119-136 (Communications in Computer and Information Science; Vol. 1752 CCIS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Evaluation of group fairness measures in student performance prediction problems
AU - Quy, Tai Le
AU - Nguyen, Thi Huyen
AU - Friege, Gunnar
AU - Ntoutsi, Eirini
N1 - The work of the first author is supported by the Ministry of Science and Culture of Lower Saxony, Germany, within the Ph.D. program “LernMINT: Data-assisted teaching in the MINT subjects”. The work of the second author is funded by the German Research Foundation (DFG Grant NI-1760/1-1), project “Managed Forgetting”.
PY - 2023/1/31
Y1 - 2023/1/31
N2 - Predicting students' academic performance is one of the key tasks of educational data mining (EDM). Traditionally, the high forecasting quality of such models was deemed critical. More recently, the issues of fairness and discrimination w.r.t. protected attributes, such as gender or race, have gained attention. Although there are several fairness-aware learning approaches in EDM, a comparative evaluation of these measures is still missing. In this paper, we evaluate different group fairness measures for student performance prediction problems on various educational datasets and fairness-aware learning models. Our study shows that the choice of the fairness measure is important, likewise for the choice of the grade threshold.
AB - Predicting students' academic performance is one of the key tasks of educational data mining (EDM). Traditionally, the high forecasting quality of such models was deemed critical. More recently, the issues of fairness and discrimination w.r.t. protected attributes, such as gender or race, have gained attention. Although there are several fairness-aware learning approaches in EDM, a comparative evaluation of these measures is still missing. In this paper, we evaluate different group fairness measures for student performance prediction problems on various educational datasets and fairness-aware learning models. Our study shows that the choice of the fairness measure is important, likewise for the choice of the grade threshold.
KW - cs.LG
KW - cs.CY
KW - Educational data mining
KW - Fairness measures
KW - Machine learning
KW - Student performance prediction
KW - Fairness
UR - http://www.scopus.com/inward/record.url?scp=85149816221&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2208.10625
DO - 10.48550/arXiv.2208.10625
M3 - Conference contribution
SN - 978-3-031-23617-4
T3 - Communications in Computer and Information Science
SP - 119
EP - 136
BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases
A2 - Koprinska, Irena
A2 - Mignone, Paolo
A2 - Guidotti, Riccardo
A2 - Jaroszewicz, Szymon
A2 - Fröning, Holger
A2 - Gullo, Francesco
A2 - Ferreira, Pedro M.
A2 - Roqueiro, Damian
A2 - Ceddia, Gaia
A2 - Nowaczyk, Slawomir
A2 - Gama, João
A2 - Ribeiro, Rita
A2 - Gavaldà, Ricard
A2 - Masciari, Elio
A2 - Ras, Zbigniew
A2 - Ritacco, Ettore
A2 - Naretto, Francesca
A2 - Theissler, Andreas
A2 - Biecek, Przemyslaw
A2 - Verbeke, Wouter
A2 - Schiele, Gregor
A2 - Pernkopf, Franz
A2 - Blott, Michaela
A2 - Bordino, Ilaria
A2 - Danesi, Ivan Luciano
A2 - Ponti, Giovanni
A2 - Severini, Lorenzo
A2 - Appice, Annalisa
A2 - Andresini, Giuseppina
A2 - Medeiros, Ibéria
A2 - Graça, Guilherme
A2 - Cooper, Lee
A2 - Ghazaleh, Naghmeh
A2 - Richiardi, Jonas
A2 - Saldana, Diego
A2 - Sechidis, Konstantinos
A2 - Canakoglu, Arif
A2 - Pido, Sara
A2 - Pinoli, Pietro
A2 - Bifet, Albert
A2 - Pashami, Sepideh
PB - Springer Nature
CY - Cham
ER -