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

• URL: http://arxiv.org/abs/2509.02774v1
• Date: Tue, 02 Sep 2025 19:21:07 GMT
• Title: Computational Social Science and Critical Studies of Education and Technology: An Improbable Combination?
• Authors: Rebecca Eynon, Nabeel Gillani,
• Abstract summary: We discuss the feasibility and desirability of the use of computational approaches as part of a critical research agenda.<n>We suggest that such approaches might help expand the capacity of critical researchers to highlight existing inequalities.<n>We focus on six areas of consideration: criticality, philosophy, inclusivity, context, classification, and responsibility.
• Score: 0.9576327614980396
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation [0.0]
  We argue that new technologies hold potential to address longstanding methodological challenges when deployed with knowledge, purpose, and ethical commitment.<n>We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand--rather than displace--the irreducible insights of qualitative research.
  arXiv  Detail & Related papers  (2025-09-15T23:12:13Z)
• Tinkering Against Scaling [15.060264126253212]
  We propose a "tinkering" approach that is inspired by existing works.<n>This method involves engaging with smaller models or components that are manageable for ordinary researchers.<n>We argue that tinkering is both a way of making and knowing for computational social science and a way of knowing for critical studies.
  arXiv  Detail & Related papers  (2025-04-23T09:21:39Z)
• Open Problems in Mechanistic Interpretability [61.44773053835185]
  Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities.<n>Despite recent progress toward these goals, there are many open problems in the field that require solutions.
  arXiv  Detail & Related papers  (2025-01-27T20:57:18Z)
• The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny? [1.0985060632689174]
  We show that a compute divide has coincided with a reduced representation of academic-only research teams in compute intensive research topics. To address the challenges arising from this trend, we recommend approaches aimed at thoughtfully expanding academic insights.
  arXiv  Detail & Related papers  (2024-01-04T01:26:11Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• Sustainability in Computing Education: A Systematic Literature Review [0.0]
  This paper describes approaches taken in computing education to address the issue of sustainability. From a set of 572 publications extracted from six large digital libraries plus snowballing, we distilled and analyzed the 90 relevant primary studies. We present a framework capturing learning objectives and outcomes as well as pedagogical methods for sustainability in computing education.
  arXiv  Detail & Related papers  (2023-05-17T16:51:37Z)
• Conceptualizing Approaches to Critical Computing Education: Inquiry, Design and Reimagination [0.0]
  Several critical issues in computing such as algorithmic bias, discriminatory practices, and techno-solutionism have become more visible. Yet, how exactly these efforts address criticality and translate it into classroom practice is not clear.
  arXiv  Detail & Related papers  (2023-04-21T15:53:11Z)
• A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
  We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
  arXiv  Detail & Related papers  (2022-12-20T18:46:16Z)
• Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing [80.72844751804166]
  In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary. We summarize the major technological points, insights, and directions that speakers brought forward during the symposium. Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
  arXiv  Detail & Related papers  (2022-10-04T17:32:27Z)
• Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions [68.6358773622615]
  This paper provides an overview of the computational and theoretical foundations of multimodal machine learning. We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
  arXiv  Detail & Related papers  (2022-09-07T19:21:19Z)
• Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation [52.903665881174845]
  We propose a pyramid of types of knowledge required in computational argumentation. We briefly discuss the state of the art on the role and integration of these types in the field.
  arXiv  Detail & Related papers  (2021-07-01T08:12:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.