Algorithmic Governance in Schools: When Data-Driven Policy Becomes Automated Exclusion
Keywords:
Algorithmic governance, educational data mining, algorithmic bias, EdTech policy, automated exclusionAbstract
This literature review examines the rapid integration of algorithmic governance in educational institutions, analyzing how data-driven tools such as predictive analytics and automated assessments impact educational equity. While frequently framed as neutral mechanisms for improving institutional efficiency and personalizing pedagogy, a critical socio-technical analysis reveals that these systems systematically reproduce and scale historical inequalities. The review identifies key mechanisms of algorithmic harm, including the reliance on flawed training data, the embedding of discriminatory proxies, and the phenomenon of "automated exclusion," wherein marginalized students are structurally rendered invisible due to data poverty. Furthermore, the delegation of authority to third-party EdTech vendors fundamentally alters institutional power dynamics, eroding teacher autonomy and creating profound accountability gaps. Ultimately, this review argues for a transition from traditional data-driven decision-making to Critical Data-Driven Decision Making (CDDDM), emphasizing the necessity of algorithmic transparency, human-in-the-loop safeguards, and liberatory design to prevent EdTech from functioning as a tool of automated inequality.
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Copyright (c) 2026 Basirun Basirun, Ari Fatihatul Hidayah, Eva Fauziyyah (Author)
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