The objective of this collaborative research between Northwestern University and University of Washington was to understand the factors that encourage success in computer-supported peer production—the form of online collaborative organization used to create public information goods like Wikipedia and Linux.  

In the past decade, a growing number of users have turned to collaborative online content as a source of information. In parallel, organizations have looked to distributed collaboration managed through virtual and volunteer communities as a source of innovation and customer support. But why do some peer production systems mobilize large communities of contributors and create valuable information goods while others do not? One answer for this challenging question is that success-related factors may change as a collaborative organization grows, such that conditions that encouraged explosive growth in the beginning may prevent further growth later.  

“The original hopes for wikis, of which there are tens of thousands beyond Wikipedia, have been only partially achieved, and they seemed to be in decline,” said NSF Program Director William Bainbridge. “Objective scientific research was needed, first to measure what was actually happening, then to develop and test hypotheses about the causes of apparent stagnation and the possible remedies that might facilitate a renaissance.”  

This project provided insights about managers of online collaborative organizations: what motivates them, how they think about change, and how they moderate and manage their communities. It also gave new knowledge of how communication and moderation affect the working of communities, as well as an ecosystem perspective on how communities compete against, compare to, and merge with other communities with similar interests. Results from the project advanced scientific understanding of collaborative organizations by testing several of the most important theories of peer production and evaluating them through large-scale longitudinal comparison of many peer production systems. It also led to valuable datasets and computational social science methods that can be taken up by other people in this space.

“This project takes a detailed look at the sense of inhabiting a different body in virtual reality: how systems can induce it, and how often they need to repeat it in order to enable lasting behavioral changes. If successful, the research is expected to provide a better understanding of stereotype threat in academic performance and insights into such threats in other disciplines like workplace team performance,” said NSF Program Director Balakrishnan Prabhakaran. 

There is a persistent shortage of healthcare workers in the USA. Occupations such as behavioral therapists, nurses, physical therapists, and home care workers are among the most in demand present and future healthcare professions. Socially assistive robots are a new strategic technology that has the potential to alleviate labor concerns in the healthcare industry by amplifying the capabilities of our existing labor force in delivering healthcare interventions to individuals in need. However, these robotic technologies remain difficult to scale and to integrate in real-world healthcare intervention delivery due to the diverse and rapidly changing needs of healthcare service consumers.  

This project led by Oakland University seeks to build enabling technologies for healthcare professionals to generate and customize robot-mediated interventions according to the needs of their consumers as well as their clinical settings. The goal is to allow healthcare professionals to teach robots communication strategies for effective intervention delivery so that socially assistive robots can provide services on par with their human teachers. Doing this may help address existing labor concerns for the healthcare industry and make healthcare services more accessible and adaptable to consumers through these socially assistive robots. The outcomes of this research will advance knowledge in effective human-robot communication, robot learning, and human-robot teaching interfaces. 

“This project has produced a number of valuable contributions to both computer science and to the autism therapy community,” said Prabhakaran. “It has also directly impacted the lives of children with autism, helping to teach them valuable social and communication skills.”   

This collaborative project led by University of Maryland, College Park, seeks to promote fair and just computational research. With the advancement and growth of technology, big data and online services, new ethical challenges have risen.  National debates have erupted over online experiments, leaked datasets, and the definition of “public” data. While investigators struggle to advise students on engaging vulnerable populations or navigating terms of service, regulators debate on how to translate traditional ethical principles into workable policy guidance.  

Research addressing these challenges has hit roadblocks caused by a lack of empirical knowledge about emerging norms and expectations. This project discovers how diverse stakeholders—big data researchers, platforms, regulators, and user communities—understand their ethical obligations and choices, and how their decisions impact data system design and use. It also compares stakeholder perspectives against the risks and realities of pervasive data itself, answering fundamental questions about the fairness and ethics of such research.  

To meet these goals, this project enables a collaboratory—a virtual center combining data and analytical resources—to collect empirical data on research ethics at diverse scopes and scales. The research includes multiple ethical issues (privacy, risk, respect, beneficence, justice) as well as the full network of stakeholders involved in research ethics (user communities, computing research communities, technical platforms, and regulations).