Teaching for a Data-Driven Future: Intentionally Building Foundational Computing Skills

被引:1
|
作者
Johnson, Amy L. [1 ,2 ]
Gleit, Rebecca D. [1 ,2 ]
机构
[1] Stanford Univ, Sociol, Stanford, CA 94305 USA
[2] Stanford Univ, Stanford, CA 94305 USA
关键词
computing; quantitative literacy; statistical software; data analysis; QUANTITATIVE LITERACY; SELF-EFFICACY; ACADEMIC-PERFORMANCE; STUDENTS; STATISTICS;
D O I
10.1177/0092055X211033632
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Despite the centrality of data analysis to the discipline, sociology departments are currently falling short of teaching both undergraduate and graduate students crucial computing and statistical software skills. We argue that sociology instructors must intentionally and explicitly teach computing skills alongside statistical concepts to prepare their students for participation in a data-driven world. We illuminate foundational concepts for computing in the social sciences and provide easy-to-integrate recommendations for building competency with these concepts in the form of a workshop designed to introduce sociology undergraduate and graduate students to the logic of statistical software. We use our workshop to show that students appreciate and gain confidence from being taught how to think about computing.
引用
收藏
页码:49 / 61
页数:13
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