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Dryad

Social network matrices data from sociology university classrooms

Cite this dataset

Love, Hannah (2023). Social network matrices data from sociology university classrooms [Dataset]. Dryad. https://doi.org/10.5061/dryad.ngf1vhhzc

Abstract

Hypothetically, a student could attend a class, listen to lectures, and pass the class without knowing or interacting with other students. What happens to the network when the classroom expectations change? For example, there is a coursework expectation that students exchange contact information, or the instructor uses collaborative learning practices. Or what if the principal investigator (PI) of a scientific team goes on a sabbatical? This study uses the framework of classrooms because of their relatability across science. We asked how do different instructor coursework expectations change network structures within a classroom or other learning environments.  A social network survey was administered at the start and end of the semester (pre- and post-test) in six university sociology classrooms to explore how expectations impacted the communication and learning networks. We found practical changes in course expectations impact the communication and learning networks, suggesting that instructors, facilitators, and others could be the archintorÔ (architect+instructor+facilitator) of the network. Understanding that expectations can impact a network’s structure marks a paradigm shift in educational assessment approaches. If the archintorÔ has identified the “optimal” network structure, then their task is to design expectations that result in specific interactions that ultimately improve student achievement and success. This work provides recommendations for classroom archintorsÔ to create the most impactful classroom networks. Future research should extend beyond education and classroom networks and identify the best or desired networks in other areas like public policy, urban planning, and more. If these “optimal” networks were identified, an archintorÔ could design a social network to solve wicked problems, manage a crisis, and create social change.

Methods

Data was collected on hardcopy surveys. Data was input to Microsoft excel and saved in CVS files.

Usage notes

Data is CSV files can be opened with Microsoft Excel or other programs that read social network matrices.  We have removed the names or participants of the data files.  If you have any questions about this study, please contact the corresponding author of this manuscript hannah.love15@alumni.colostate.edu.

 

Missing values: 

0 = no value given in social network survey 

blank = no data given in social network survey

Cell A1 is intentionally blank in social network analysis matrices

Recognize network for CBR Social Change is missing