Understanding and improving social factors in education: a computational social science approach

Book section : Chapter

Over the past decade, an explosion in the availability of education-related datasets has enabled new computational research in education. Much of this work has investigated digital traces of online learners in order to better-understand and optimize their cognitive learning processes. Yet cognitive learning on digital platforms does not equal education. Instead, education is an inherently social, cultural, economic, and political process manifesting in physical spaces, and educational outcomes are influenced by many factors that precede and shape the cognitive learning process. Many of these are social factors like children’s connections to schools (including teachers, counselors, role models), parents and families, and the broader neighborhoods in which they live. In this article, we briefly discuss recent studies of learning through large-scale digital platforms, but largely focus on those exploring sociological aspects of education. We believe computational social scientists can creatively advance this emerging research frontier—and in doing so, help facilitate more equitable educational and life outcomes.

Authors: Gillani, N; Eynon, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Edward Elgar Publishing
• Host title: Handbook of Computational Social Science
• Pages: 872-890
• Chapter number: 57
• Place of publication: Cheltenham
• Publication date: 2025-12-11
• Edition: 1
• DOI: https://doi.org/10.4337/9781802207309.00078
• EISBN: 9781802207309
• ISBN: 9781802207293
• Language: English
• Keywords: education; learning analytics; neighborhood effects; social data science; educational data science
• Subtype: Chapter