NDLI: Uncovering Trajectories of Informal Learning in Large Online Communities of Creators

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Uncovering Trajectories of Informal Learning in Large Online Communities of Creators

Content Provider	ACM Digital Library
Author	Domeniconi, Carlotta Johri, Aditya Beckley, Chris Yang, Seungwon Revelle, Matt Sweeney, Mack Gelman, Ben U.
Abstract	We analyzed informal learning in Scratch Online -- an online community with over 4.3 million users and 6.7 million user-generated content. Users develop projects, which are graphical interfaces involving manipulation of programming blocks. We investigated two fundamental questions: how can we model informal learning, and what patterns of informal learning emerge. We proceeded in two phases. First, we modeled learning as a trajectory of cumulative programming block usage by long-term users who created at least 50 projects. Second, we applied K-means++ clustering to uncover patterns of learning and corresponding subpopulations. We found four groups of users manifesting four different patterns of learning, ranging from the smallest to the largest improvement. At one end of the spectrum, users learned more and in a faster manner. At the opposite end, users did not show much learning, even after creating dozens of projects. The modeling and clustering of trajectory patterns that enabled us to quantitatively analyze informal learning may be applicable to other similar communities. The results can also support administrators of online communities in implementing customized interventions for specific subpopulations.
Starting Page	131
Ending Page	140
Page Count	10
File Format	PDF
ISBN	9781450334112
DOI	10.1145/2724660.2724674
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Online community Learning analytics Scratch Programming Clustering Informal learning Modeling
Content Type	Text
Resource Type	Article

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