We present results from an ethnographic inquiry of technology-based learning at an after-school learning center in Mumbai (India) that caters to students from neighboring slum communities.We conducted participant observation for 120 hours and 58 semi-structured interviews with different stakeholders (including teachers, staff, parents, and students) at the center over nine weeks from December 2015 to July 2016. Taking an assets-based approach in an underserved context, we uncover the role of care as a resource and present the rich and varied caring behaviors enacted in this sociotechnical system.We then discuss how care effects a greater sense of ownership, interdependency, and community. Examining the role of aligning values in motivating caring behavior, we conclude with recommendations for supporting, leveraging, and extending care via technology design in an underserved, technology-enhanced learning environment.

The development and adoption of new protocols (or of extensions to existing protocols) is arguably central to the Internet's evolution. However, and in spite of over 40 years of experience with this process, we have limited understanding of what factors may contribute to a protocol's success. A sound technical design and a well-grounded purpose are obviously important, but we have many examples of failures that met those two criteria. What other factors affect a protocol's likelihood of success, and under what circumstances? We investigate this question through a statistical approach, based on analyzing a set of about 250 Internet standard documents, Internet engineering task force request for comments (RFCs). We characterize these RFCs using a number of key features, which we then seek to associate with positive or negative odds when it comes to success. Our high-level results are intuitive, e.g., protocols that call for Internet-wide adoption face greater challenges. Focusing on more targeted subsets of protocols reveals more subtle and possibly more interesting differences between areas of the Internet landscape. We also apply our prediction framework to IPv6, and use different “what-if” scenarios to explore what might have affected its deployment.