AI-based prediction of academic success: Support for many, disadvantage for some?
The use of computational tools to predict academic success has become increasingly popular. Machine learning algorithms, trained on past study histories, have been shown to provide valid predictions. However, knowing about biases and unfairness in algorithms, one should take a closer look at these p...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Computers and Education: Artificial Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X24001061 |
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| author | Lisa Herrmann Jonas Weigert |
| author_facet | Lisa Herrmann Jonas Weigert |
| author_sort | Lisa Herrmann |
| collection | DOAJ |
| description | The use of computational tools to predict academic success has become increasingly popular. Machine learning algorithms, trained on past study histories, have been shown to provide valid predictions. However, knowing about biases and unfairness in algorithms, one should take a closer look at these predictions. This paper explores the extent to which the predictive accuracy of academic success varies between specific groups of students, focusing on traditional and non-traditional students (NTS), who have not acquired a higher education entrance qualification at school. In a case study the study compares several popular algorithms and their prediction quality, and investigates whether misclassified NTS show positive or negative biases. Results revealed that the accuracy of predicting academic success for NTS was significantly lower than when considering all students as a whole. The direction of the distortion cannot be determined exactly due to small case numbers. The study emphasizes that the possibility of bias always has to be considered when predicting study success, and the use of such tools must ensure there are no undesirable biases that could affect certain students. |
| format | Article |
| id | doaj-art-0b6b09fa6f494676b0f6290b2ab8d3b5 |
| institution | Kabale University |
| issn | 2666-920X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-0b6b09fa6f494676b0f6290b2ab8d3b52024-12-19T11:01:33ZengElsevierComputers and Education: Artificial Intelligence2666-920X2024-12-017100303AI-based prediction of academic success: Support for many, disadvantage for some?Lisa Herrmann0Jonas Weigert1Corresponding author.; Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Business Education and Human Resource Development, Lange Gasse 20, 90403, Nürnberg, GermanyFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Business Education and Human Resource Development, Lange Gasse 20, 90403, Nürnberg, GermanyThe use of computational tools to predict academic success has become increasingly popular. Machine learning algorithms, trained on past study histories, have been shown to provide valid predictions. However, knowing about biases and unfairness in algorithms, one should take a closer look at these predictions. This paper explores the extent to which the predictive accuracy of academic success varies between specific groups of students, focusing on traditional and non-traditional students (NTS), who have not acquired a higher education entrance qualification at school. In a case study the study compares several popular algorithms and their prediction quality, and investigates whether misclassified NTS show positive or negative biases. Results revealed that the accuracy of predicting academic success for NTS was significantly lower than when considering all students as a whole. The direction of the distortion cannot be determined exactly due to small case numbers. The study emphasizes that the possibility of bias always has to be considered when predicting study success, and the use of such tools must ensure there are no undesirable biases that could affect certain students.http://www.sciencedirect.com/science/article/pii/S2666920X24001061Academic analyticsMachine learningAlgorithmic biasStudent successPredicting successNon-traditional students |
| spellingShingle | Lisa Herrmann Jonas Weigert AI-based prediction of academic success: Support for many, disadvantage for some? Computers and Education: Artificial Intelligence Academic analytics Machine learning Algorithmic bias Student success Predicting success Non-traditional students |
| title | AI-based prediction of academic success: Support for many, disadvantage for some? |
| title_full | AI-based prediction of academic success: Support for many, disadvantage for some? |
| title_fullStr | AI-based prediction of academic success: Support for many, disadvantage for some? |
| title_full_unstemmed | AI-based prediction of academic success: Support for many, disadvantage for some? |
| title_short | AI-based prediction of academic success: Support for many, disadvantage for some? |
| title_sort | ai based prediction of academic success support for many disadvantage for some |
| topic | Academic analytics Machine learning Algorithmic bias Student success Predicting success Non-traditional students |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X24001061 |
| work_keys_str_mv | AT lisaherrmann aibasedpredictionofacademicsuccesssupportformanydisadvantageforsome AT jonasweigert aibasedpredictionofacademicsuccesssupportformanydisadvantageforsome |