A Social Learning Network, or SLN, encapsulates a range of scenarios in which a number of people learn from one another through structured interaction. These are networks that exist between learners, instructors, and also modules of information.
The proliferation of online learning, and online communication in general, has given rise to a number of SLN applications in recent years. Some of these include online education, enterprise social networks for online training, and Q&A sites, which have created SLN among students, employees, and askers/answerers, respectively.
In this work, we ask: How efficient are real-world SLN, and how can their efficiencies be improved? To answer these questions, we have developed a framework in which SLN efficiency is determined by comparing user benefit in the observed network to a benchmark of maximum utility achievable through optimization. Our framework defines the optimal SLN through utility maximization subject to a set of constraints that can be inferred from the network.
Through evaluation on four MOOC discussion forum datasets and optimizing over millions of variables, we find that SLN efficiency can be rather low (from 68% to 82% depending on the specific parameters and dataset), which indicates that much can be gained through optimization. We find that the gains in global utility (i.e., average across users) can be obtained without making the distribution of local utilities (i.e., utility of individual users) less fair. We have also proposed a method for curating news feed in online SLN to realize the optimal networks in practice.
For more information, feel free to email Christopher Brinton at cbrinton [at] princeton [dot] edu, and/or check out our paper published in INFOCOM 2016.