Using Social Network Analysis to Better Understand Student Connections and Learning

 

Tested Studies in Laboratory Teaching, 2018, Volume 39

Dylan Dittrich-Reed

Abstract

In laboratories and other active learning environments, learning is embedded in a social context: students learn with and from the students with whom they interact. According to social capital theory, students with more social connections to other students have greater access to the information and skills of their peers, which promotes learning and academic success. Instructors who can monitor social connections among their students can better understand how information flows through a classroom and whether all students have access to that flow of information. However, identifying and quantifying student social connections can be difficult without the proper tools. Social Network Analysis is a set of techniques and statistical methods that quantify and visualize connections between students. In this workshop, participants will learn how to collect and interpret social network data. Participants will learn the basics of social network theory as well as methods for surveying students to collect meaningful social network data. Additionally, participants will work in small groups with sample classroom networks to practice interpreting network graphs and statistics. Interested participants will be provided with resources to help them learn how to use the necessary applications to perform the analyses on their own.

Keywords:  student learning, social network analysis, social capital, diffusion of innovations

University of Wisconsin, Madison (2017)