See also: An Introduction to SNA using R and NetDraw, SNA & Predictive Modeling, Using Twitter to Demonstrate Basic Concepts from SNA, An Introduction to SNA with Applications
Social Psychology of Education
June 2012, Volume 15, Issue 2, pp 165-180
A social network analysis of student retention using archival data
James E. Eckles,
Eric G. Stradley
|This study attempts to determine if a relationship exists between first-to-second-year retention and social network variables for a cohort of first-year students at a small liberal arts college. The social network is reconstructed using not survey data as is most common, but rather using archival data from a student information system. Each student is given a retention score and an attrition score based on the behavior of their immediate relationships in the network. Those scores are then entered into a logistic regression that includes tradition background and performance variables that are traditionally significantly related to retention. Students' friends' retention and attrition behaviors are found to have a greater impact on retention that any background or performance variable.|
JOURNAL OF COLLEGE STUDENT RETENTION, Vol. 4(1) 39-52, 2002-2003
THE ROLE OFSOCIAL SUPPORT NETWORK
IN COLLEGE PERSISTENCE AMONG
MICHAEL P. SKAHILL
University of San Francisco, California
Used social network analysis to examine the role of social support networks in student persistence among residential and commuter students. Found that commuter students are less likely to persist, while residential students who reported making greater numbers of new friends with connections to the school also reported attaining personal and academic goals at a significantly greater rate.
The Journal of Higher Education
Vol. 71, No. 5, Sep. - Oct., 2.
Ties That Bind: A Social Network Approach to Understanding Student Integration and Persistence
Scott L. Thomas
This study examined the social networks of college students and how such networks affect student commitment and persistence. The study's theoretical framework was based on application of the social network paradigm to Tinto's Student Integration Model, in which a student's initial commitment is modified over time as a result of the student's integration into the campus community. Freshmen enrolled for the spring 1993 semester responded (322 of 379) to the First-Year Experiences Survey, which involved identifying students with whom they frequently spoke and the dimensions on which they related to these students. Results were compared with enrollment data for the fall 1993 semester to identify students returning for their sophomore year. The largest effect on persistence was associated with the number of nominations received from other students, and this factor operated indirectly through enhanced social integration, institutional commitment, and intention. Overall, students with broader, well-connected networks were more likely to persist, whereas students with a higher proportion of ties falling within their social peer group were less likely to persist.
Physics Education Research Conference 2009
Part of the PER Conference series
Ann Arbor, Michigan: July 29-30, 2009
Volume 1179, Pages 105-108
Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center written by Eric Brewe, Laird H. Kramer, and George O'Brien
Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. Finn and Rock  link the academic and social integration of students to increased rates of retention. We utilize social network analysis to quantify interactions in Florida International University's Physics Learning Center (PLC) that support the development of academic and social integration,. The tools of social network analysis allow us to visualize and quantify student interactions, and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors which contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of supportive learning community.