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

TL;DR

This paper emphasizes the importance of social, cultural, and political factors in education, advocating for computational social science methods to enhance understanding and promote equity in educational outcomes.

Contribution

It highlights the need to incorporate social factors into computational education research and suggests that social science approaches can improve educational equity.

Findings

Digital traces reveal cognitive learning patterns but do not capture social aspects.

Social connections significantly influence educational outcomes.

Computational social science can advance understanding of social factors in education.

Abstract

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, and 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.