Advancing Social Influence Models in Learning Analytic
Joshua M. Rosenberg, University of Tennessee, Knoxville
Bret Staudt Willet, Michigan State University (next, Florida State University)
We are often impacted by our networks
1We feel very old writing this
These impacts can be seen in terms of influence processes
Influence has long been a key construct in network analysis (Sweet, 2018).
For instance, sociologists developing the social influence approach used statistical models to understand how social capital (i.e., resources inherent to and available through relationships) exerted power (Bourdieu, 1980)
In short, influence may be thought of in terms of how individuals affect one another (Frank, 1998)
Another way to consider influence is in the following terms:
Network -> Individuals’ actions, beliefs, and achieved outcomes
Distinction from another technique - selection
Selection models aim to understand who interacts with whom (Fincham et al., 2018) - these models are different from but related to those we estimate when concerned with influence
These selection processes are contemporarily estimated using powerful extensions of inferential statistical techniques such as logistic regressions, Exponential Random Graph Models (e.g., Gašević et al., 2019)
Another way to consider selection is in the following terms:
Individuals’ actions and beliefs -> A network
Our conjecture
Our central argument here is that influence models are especially valuable because they allow researchers to interrogate what is intuitively important about networks
That is, social networks—those of teachers, administrators, researchers—can influence actions, behaviors, and learning
Using centrality measures as proxies for influential individuals, especially with cross-sectional data, introduce problems with respect to causality (Sweet, 2018)
Examples of influence from others’ work
Frank et al. (2004) examined how the use of innovative digital technologies, namely the use of computers for different purposes, were adopted by teachers throughout a district when teachers identified as leaders among their peers adopted and used them
Collected network data from all of the teachers in the district by asking them to nominate up to ten individuals who they go to for help
Determined how much of the variability in teachers’ use of computer technologies depended upon who they said they went to for help over the preceding year
Found that more variance in computer use was explained by social influence measures—who teachers went to for help—than the more traditional, psychologically-focused measures of teachers’ value for computers
An example from our own work: #NGSSchat
Goal related to influence: Understand how participation in #NGSSchat conversations across an entire year could explain the rate of participation in the following year
Calculating exposure to others involved determining the number of times every other individual interacted with each individual, and then multiplying that number by in-degree centrality; we then summed these to create a total value-or exposure term-for each individual
In sum, our influence model:
Predicted the number of posts individuals sent in the subsequent year on the basis of an exposure term reflecting their involvement in conversations with central individuals
How the same network could be amenable to selection questions
An example from our own work: #NGSSchat
We found that individuals’ exposure to conversations with central individuals in the previous year was positively associated with greater sustained participation in the next year
Specifically, for every one-SD increase in the number of conversations in which an individual participated, individuals were likely to post 9-15 additional tweets (in log-odds units, β’s = 1.43 – 1.83, p < .001)
In sum, our analysis of Twitter #NGSSchat showed that involvement in conversations (similar to Horn et al., 2020) predicted an outcome—later participation
Future directions
Question (and punchline): Should we explore both influence and other aspects of networks?
(we think so, but we’re new to this)
Questions for the field
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