Computational Social Science and Critical Studies of Education and Technology: An Improbable Combination?

2025-09-02

AI Summary

This study addresses the urgent need to integrate computational methods effectively into critical educational technology research to advance educational equity. Methodologically, it synthesizes machine learning, critical data studies, and Science and Technology Studies (STS) theory to develop and empirically validate a social justice–oriented computational methodology, grounded in six core principles: criticality, philosophical reflexivity, inclusivity, contextual embeddedness, categorical self-reflection, and shared accountability. Drawing on two empirical case studies, the framework successfully uncovers structural educational inequities, informs equitable intervention design, and empowers marginalized communities to participate meaningfully in technology development and deployment. Its key contribution lies in reconfiguring computational methods from instrumental tools to critical practices—thereby enabling deep alignment between technical capacity and social justice aims. The study offers both theoretical grounding and actionable pathways for paradigmatic transformation in computational social science approaches to educational equity.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Computational social science
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

As belief around the potential of computational social science grows, fuelled by recent advances in machine learning, data scientists are ostensibly becoming the new experts in education. Scholars engaged in critical studies of education and technology have sought to interrogate the growing datafication of education yet tend not to use computational methods as part of this response. In this paper, we discuss the feasibility and desirability of the use of computational approaches as part of a critical research agenda. Presenting and reflecting upon two examples of projects that use computational methods in education to explore questions of equity and justice, we suggest that such approaches might help expand the capacity of critical researchers to highlight existing inequalities, make visible possible approaches for beginning to address such inequalities, and engage marginalised communities in designing and ultimately deploying these possibilities. Drawing upon work within the fields of Critical Data Studies and Science and Technology Studies, we further reflect on the two cases to discuss the possibilities and challenges of reimagining computational methods for critical research in education and technology, focusing on six areas of consideration: criticality, philosophy, inclusivity, context, classification, and responsibility.

Problem

• Combining computational methods with critical education technology studies
• Exploring feasibility of computational approaches for equity research
• Addressing challenges in applying data science to educational justice

Innovation

• Combining computational methods with critical research
• Using computational approaches for equity and justice
• Reimagining computational methods for critical education studies