The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.

翻译：高等教育领域中使用算法进行决策的情况持续增长，既为机构节省成本、为学生提供个性化服务带来前景，也引发了关于监督、公平性和数据解释等方面的伦理挑战。为解决当前缺乏对这些算法设计方式系统性理解的问题，我们回顾了大量提出高等教育决策算法的论文。我们根据输入数据、计算方法和目标结果对其进行分类，进而探究这些因素与理论、参与式或推测性设计等人本化视角应用的相互关系。研究发现，模型正趋向于使用深度学习，且越来越多地涉及学生个人数据和受保护属性，目标范围也在向自动化决策扩展。然而，尽管可解释性和可说明性随之降低，当前的发展仍未能充分融入人本化视角。我们讨论了这些趋势带来的挑战，并倡导采用人本化的方法。