AI-based prediction of academic success: Support for many, disadvantage for some?

Abstract

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.

Keywords

• Academic analytics
• Machine learning
• Algorithmic bias
• Student success
• Predicting success
• Non-traditional students