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RAPP: A Responsible APP Tool for Decision-Making in Educational Institutes

full text: PDF
author/s: Manh Khoi Duong, Jannik Dunkelau, José Andrés Cordova, Stefan Conrad
editor:B. König-Ries et al.
booktitle:Datenbanksysteme für Business, Technologie und Web (BTW 2023)

Due to the increasing importance of educational data mining for the early intervention of at-risk students and the growth of performance data collected in educational institutes, it becomes natural to employ machine learning models to predict student's performances based off prior data. Although machine learning pipelines are often similar, developing one for a specific target prediction of academic success can become a daunting task. In this work, we present a graphical user interface which implements a customizable machine learning pipeline which allows the training and evaluation of machine learning models for different definitions of academic success, \eg, collected credits, average grade, number of passed exams, etc. The evaluation is exported in PDF format after finishing training. As this tool serves as a decision support system for socially responsible AI systems, fairness notions were included in the evaluation to detect potential discrimination in the data and prediction space.

Heinrich Heine Universität

Datenbanken und Informationssysteme


Prof. Dr. Stefan Conrad

Universitätsstr. 1
40225 Düsseldorf
Gebäude: 25.12
Etage/Raum: 02.24
Tel.: +49 211 81-14088


Lisa Lorenz

Universitätsstr. 1
40225 Düsseldorf
Gebäude: 25.12
Etage/Raum: 02.22
Tel.: +49 211 81-11312
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