Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms

• Published in: Technology, knowledge and learning knowledge and learning, 2024-06, Vol.29 (2), p.1079-1107
• Main Authors: Lünich, Marco, Keller, Birte, Marcinkowski, Frank
• Format: Article
• Language: English
• Subjects: Academic achievement; Agents (artificial intelligence); Artificial intelligence; Creativity and Arts Education; Decision making; Design factors; Distributive justice; Education; Educational Technology; Factorial design; Feedback; Graduation Rate; Higher education; Higher education institutions; Learning and Instruction; Norms; Original Research; Performance prediction; Science Education; STEM education; Students; Technical education; Young adults
• ISSN: 2211-1662; 2211-1670
• DOI: 10.1007/s10758-023-09698-y

Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students’ fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality , equity , and need . Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 × 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against potential risks, and establish continuous feedback through ongoing consultation with all stakeholders.