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.
翻译:过去十年来,与教育有关的数据集的提供急剧增加,使得教育领域出现了新的计算研究。许多这类工作都调查了在线学习者的数字痕迹,以便更好地了解和优化他们的认知学习过程。然而,在数字平台上的认知学习并不等于教育。相反,教育是一个内在的社会、文化、经济和政治过程,表现在物理空间,教育成果受到认知学习过程之前和形成过程的许多因素的影响。其中许多是社会因素,如儿童与学校的联系(包括教师、顾问和榜样)、父母和家庭以及他们居住的更广泛的社区。在本篇文章中,我们简要讨论了最近通过大型数字平台进行的学习研究,但主要侧重于探索教育的社会层面。我们认为,计算社会科学家可以创造性地推进这种新兴的研究前沿,并且这样做可以帮助促进更公平的教育和生活成果。