This FOCUS cluster pursues themes associated with Duke's strategic goal of using knowledge in the service of society. Increasingly in American higher education, both public and private institutions are underscoring their commitment to civic engagement in their curricular and co-curricular offerings. This stems from an increased recognition of the important role that our colleges and universities play in helping to solve real world problems in our local, national and international communities. Duke’s commitment to civic engagement has been widely recognized, and students in this cluster will become widely familiar with how Duke is engaged with it is many communities.
Students enrolling in the FOCUS cluster will learn about, critique, and explore the conceptual underpinnings of the pursuit of civic engagement and will get real life experiences through a service learning course that places students in a Durham public school and a leadership course that provides students the opportunity to develop and act on enterprising civic engagement ideas.
Susie Post-Rust, Lecturing Fellow of Documentary Studies
Few experiences are more vital than childhood or more reflective than retirement. Using digital photography and a documentary approach, students document one of Durham's public schools or the Croasdaile Village Retirement Community. Students learn to use cameras, and through a semester-long project explore meaningful topics, give voice to subjects, and think about issues that grow out of change. Collectively the students’ body of work portrays the life of the school and community, and in so doing, mirrors course content across the FOCUS cluster. As part of Service-Learning, culmination of the class includes a student exhibit and website launch that takes place publicly at the school; prints and the project website reside at the school. Course includes discussion on ethics in documentary photography, basic camera usage, Photoshop, narrative storytelling, and dissemination. Service-Learning Course (SLCE).
Zoila Airall, Adjunct Associate Professor of Education
This course features a critical exploration of how structural racism intersects with educational systems. Students will begin with an examination of identity, positionality, privilege, and bias at the individual level, eventually moving toward an analysis of institutional structures and systems. Students will reflect on their own educational experiences and apply a case study analysis to their K-12 education utilizing the framework of critical race theory. Students will study data on educational inequities and work in teams to prepare a policy proposal that addresses racial inequities within K-12 education.
Amy Anderson, Assistant Professor of the Practice of Education
In 1954 the Supreme Court case Brown versus the Board of Education forever changed American schools by ending segregation and creating educational equity. Or did it? Are today's schools any more inclusive or socially just than schools were 50 years ago? Examination of ways schools may or may not perpetuate and reproduce social inequities. Focus on recent efforts to imagine and create socially-just schools. Discussion of our ethical responsibilities as civically engaged citizens to work towards educational equality and provide support of schools that are inclusive, culturally responsive, and democratic. Required service-learning experience working with children in a Durham public school. Service-Learning Course (SLCE).
Mine Cetinkaya-Rundel, Professor of the Practice of Statistical Science
This class will teach you how to use modern data science tools to visualize data and, through the lens of visualization, it will introduce you to programming and data science concepts and workflows. Working with data on issues of local, national, and global societal importance, we will learn to create elegant, insightful, and impactful data visualizations through iterative and reproducible processes. We will also discuss the role of visualization in exploratory data analyses as well as in communicating the results of statistical inference and modeling. The course will focus on the R statistical computing language. No statistical or computing background is necessary.