  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.
