Abstract
This study investigates the influence of performance and diversity factors on student success using machine learning models. Two case studies from Austrian universities are presented, comparing the predictive power of models with and without diversity related factors. While performance indicators seem to have larger impact on student success, diversity factors can slightly improve model accuracy and help identify at-risk students. However, the importance of the use of diversity indicators in predictive models varies depending on the study program, the student population and on the aim with which the analysis is carried out. The study highlights the potential and limitations of using machine learning models to predict student success and emphasizes the need for context-specific analysis to avoid generalization and ensure fair and effective interventions.
Originalsprache | Englisch |
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Titel des Sammelwerks | 28th International Conference on Science, Technology and Innovation Indicators (STI2024) |
Untertitel des Sammelwerks | Berlin, Germany, 18-20 September 2024 |
Verlag | Fraunhofer Institute for Systems and Innovation Research |
Seitenumfang | 15 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2024 |