This research will develop and design new data-driven risk prediction principles and management (DDRPM) tools that anticipate and manage a variety of community risks, which fire departments are increasingly required to respond to, including medical, fire, and safety emergencies. Today, much of their work focuses on community risk reduction (CRR), a paradigm that seeks to mitigate risks before they lead to emergencies in the first place. The CRR paradigm will leverage new data-driven risk prediction and management (DDRPM) tools to predict and respond to a variety of community risks.
Social services, traditionally, have been organized around their missions, such as education or safety or health. A newer approach, called "wrap-around services" or "systems of care," organizes services around individuals and their specific context and needs. These systems face many challenges when applied in real-world settings. Application processes often focus more on the potential of technologies and less on the realities, histories, and needs of communities. The proposed research addresses this gap by evaluating the implementation of a system of care in a real-world setting.
Value sensitive design (VSD) is a methodology that focuses on examining potential stakeholders' values and establishing designers' values of ethical imports in designing a technological system. While this approach provides effective ways to incorporate users' values in technology design, understanding teachers' values in culturally responsive teaching (CRT) poses unique challenges due to their interactions with students' cultural identities, school environments, and community contexts.
Culturally Responsive Teaching (CRT) is a set of instructional practices that acknowledges and incorporates students’ identities and backgrounds into the classroom in a way that makes learning more effective and relevant for culturally, rhetorically, and ethnically diverse students. As classrooms becoming more diverse, recognizing and celebrating students’ cultural traits and characteristics are becoming increasingly effective to cultivating positive educational outcomes.
COVID-19 has killed hundreds of thousands, constituting a major global crisis. Laypeople bear a large burden of responsibility for perceiving risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information.
KNEXT is a three-year collaborative project between Kent State University (KSU-SLIS) and the University of Maryland (UMD-CIS), which partners with local public libraries, small business development centers, economic development organizations, and community advocacy groups to bring advanced data analytics and business intelligence (DA&BI) services to public libraries in order to support small businesses, entrepreneurs, and community advocates within two recovering communities in Ohio and Maryland.
In 2010, a team of graduate students from Philosophy, Design, Electrical Engineering, and Mechanical Engineering worked together to design a home service robot of the future. We initially started with using the Taguchi method, a robust hardware design method, to list the necessary functions of the future robot. Subsequently, we had regular ideation/brainstorming sessions iteratively to come up with future robots that will operate in an automated household environment.
This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing.
To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors and contexts by taking a socio-technical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that shape people’s access to information.
Urban renewal was a national initiative from the 1960s through 70s aimed at improving so-called “blighted” areas, and resulted in the displacement of many vibrant communities. While the underlying mechanisms of urban renewal have been examined, there have been very few data-driven, evidence-based studies that take into account the histories and interests of former residents. The “Human Face of Big Data” project started as a digital curation effort to design and develop a web-based, big data platform that provides insights and analytics into the mechanisms of this process.