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

IF 3 Q1 EDUCATION & EDUCATIONAL RESEARCH

Technology Knowledge and Learning Pub Date : 2023-11-11 DOI:10.1007/s10758-023-09698-y

Marco Lünich, Birte Keller, Frank Marcinkowski

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引用次数: 0

Abstract

Abstract 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 $$\times$$ × 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.

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学业成绩预测对学生支持措施分配的公平性:分配正义规范感知公平性的差异

人工智能在高等教育中的应用越来越普遍，因为它有望改善和加速与学生支持有关的行政流程，旨在提高学生的成功率和毕业率。例如，学业成绩预测(APP)提供个人反馈，并作为分发学生支持措施的基础。然而，APP的使用及其所有挑战(例如，固有偏见)显著影响了年轻人的未来前景。因此，仔细权衡这些系统的机会和风险，并让受影响的学生参与开发阶段是很重要的。本研究基于APP系统探讨学生对支持措施分配的公平感。首先，我们研究学生如何评估三种不同的分配正义规范，即平等、公平和需要。其次，我们调查了基于人类或算法决策的APP之间的公平感知是否存在差异。第三，我们讨论了科学、技术、工程和数学(STEM)专业的学生与社会科学、人文科学和艺术(SHAPE)专业的学生之间的评估是否存在差异。为此，我们对n = 1378名德国学生进行了2 $$\times$$ × 3因子设计的横断面调查，其中我们使用不同分布规范和决策代理作为设计因素。我们的研究结果表明，学生更喜欢基于平等的支持措施分布，这种偏好不受APP是基于人为决策还是基于算法决策的影响。此外，除了STEM学科的学生认为基于需求规范的分配比SHAPE学科的学生更公平外，学习领域对公平感知没有影响。基于这些发现，高等教育机构在考虑APP时应优先考虑以学生为中心的决策，权衡实际需求与潜在风险，并通过与所有利益相关者的持续协商建立持续的反馈。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technology Knowledge and Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.50

自引率

6.10%

发文量

期刊介绍： Technology, Knowledge and Learning emphasizes the increased interest on context-aware adaptive and personalized digital learning environments. Rapid technological developments have led to new research challenges focusing on digital learning, gamification, automated assessment and learning analytics. These emerging systems aim to provide learning experiences delivered via online environments as well as mobile devices and tailored to the educational needs, the personal characteristics and the particular circumstances of the individual learner or a (massive) group of interconnected learners. Such diverse learning experiences in real-world and virtual situations generates big data which provides rich potential for in-depth intelligent analysis and adaptive feedback as well as scaffolds whenever the learner needs it. Novel manuscripts are welcome that account for how these new technologies and systems reconfigure learning experiences, assessment methodologies as well as future educational practices. Technology, Knowledge and Learning also publishes guest-edited themed special issues linked to the emerging field of educational technology. Submissions can be empirical investigations, work in progress studies or emerging technology reports. Empirical investigations report quantitative or qualitative research demonstrating advances in digital learning, gamification, automated assessment or learning analytics. Work-in-progress studies provide early insights into leading projects or document progressions of excellent research within the field of digital learning, gamification, automated assessment or learning analytics. Emerging technology reports review new developments in educational technology by assessing the potentials for leading digital learning environments. Manuscripts submitted to Technology, Knowledge and Learning undergo a blind review process involving expert reviews and in-depth evaluations. Initial feedback is usually provided within eight weeks including in progress open-access abstracts and review snapshots.