"This volume speaks of the multiplicity of data required to tell the community college story. The authors explore and detail how various sources - workforce data, market data, state-level data, federal data, and, of course, institutional data such as transcript files - all have something to say about the life of a community college"--Back cover.

Internal and external pressure continues to mount for college professionals to provide evidence of successful activities, programs, and services, which means that, going forward, nearly every campus professional will need to approach their work with a data-informed perspective.But you find yourself thinking “I am not a data person”.Yes, you are. Or can be with the help of Amelia Parnell.You Are a Data Person provides context for the levels at which you are currently comfortable using data, helps you identify both the areas where you should strengthen your knowledge and where you can use this knowledge in your particular university role.For example, the rising cost to deliver high-quality programs and services to students has pushed many institutions to reallocate resources to find efficiencies. Also, more institutions are intentionally connecting classroom and cocurricular learning experiences which, in some instances, requires an increased gathering of evidence that students have acquired certain skills and competencies. In addition to programs, services, and pedagogy, professionals are constantly monitoring the rates at which students are entering, remaining enrolled in, and leaving the institution, as those movements impact the institution’s financial position.From teaching professors to student affairs personnel and beyond, Parnell offers tangible examples of how professionals can make data contributions at their current and future knowledge level, and will even inspire readers to take the initiative to engage in data projects.The book includes a set of self-assessment questions and a companion set of action steps and available resources to help readers accept their identity as a data person. It also includes an annotated list of at least 20 indicators that any higher education professional can examine without sophisticated data analyses.

People providing services to schools, teachers, and students want to know whether these services are effective. With that knowledge, a project director can expand services that work well and adjust implementation of activities that are not working as expected. When finding that an innovative strategy benefits students, a project director might want to share that information with other service providers who could build upon that strategy. Some organizations that fund programs for students will want a report demonstrating the program’s success. Determining whether a program is effective requires expertise in data collection, study design, and analysis. Not all project directors have this expertise—they tend to be primarily focused on working with schools, teachers, and students to undertake program activities. Collecting and obtaining student-level data may not be a routine part of the program. This book provides an overview of the process for evaluating a program. It is not a detailed methodological text but focuses on awareness of the process. What do program directors need to know about data and data analysis to plan an evaluation or to communicate with an evaluator? Examples focus on supporting college and career readiness programs. Readers can apply these processes to other studies that include a data collection component.

Brad C. Phillips and Jordan E. Horowitz offer a research-based model and actionable approach for using data strategically at community colleges to increase completion rates as well as other metrics linked to student success. They draw from the fields of psychology, neuroscience, and behavioral economics to show how leaders and administrators can build good habits for engaging with data constructively. At the core of their approach is a strategic effort to help administrators and faculty identify leading indicators that they can affect and monitor before student failure occurs. The book also helps educators make better use of common sources of data, clarify problems to be solved, match research-based interventions to problems, and evaluate results. The authors incorporate strategies for college personnel to engage with data more effectively by integrating student stories into presentations and embedding these discussions into existing meetings and routines. Three case studies from Long Beach City College, Southwestern College, and Odessa College further illustrate how this approach was implemented as part of comprehensive reform efforts. Based on two decades of experience working with colleges across the country, Creating a Data-Informed Culture in Community Colleges promises to be a valuable contribution to the ongoing conversation about information use in education to improve student outcomes.

In the United States, 1,200 community colleges enroll over ten million students each year—nearly half of the nation’s undergraduates. Yet fewer than 40 percent of entrants complete an undergraduate degree within six years. This fact has put pressure on community colleges to improve academic outcomes for their students. Redesigning America’s Community Colleges is a concise, evidence-based guide for educational leaders whose institutions typically receive short shrift in academic and policy discussions. It makes a compelling case that two-year colleges can substantially increase their rates of student success, if they are willing to rethink the ways in which they organize programs of study, support services, and instruction. Community colleges were originally designed to expand college enrollments at low cost, not to maximize completion of high-quality programs of study. The result was a cafeteria-style model in which students pick courses from a bewildering array of choices, with little guidance. The authors urge administrators and faculty to reject this traditional model in favor of “guided pathways”—clearer, more educationally coherent programs of study that simplify students’ choices without limiting their options and that enable them to complete credentials and advance to further education and the labor market more quickly and at less cost. Distilling a wealth of data amassed from the Community College Research Center (Teachers College, Columbia University), Redesigning America’s Community Colleges offers a fundamental redesign of the way two-year colleges operate, stressing the integration of services and instruction into more clearly structured programs of study that support every student’s goals.

Education has fought long and hard to gain acceptance as a profession and, since professionals by definition use data to shape the decisions they make, education has little choice but to continue moving in this direction. This 3-part handbook represents a major contribution to the literature of education. It is a unique compendium of the most original work currently available on how, when and why evidence should be used to ground practice. It is a comprehensive, cross-disciplinary, research-based, and practice-based resource that all educators can turn to as a guide to data-based decision making. The Handbook of Data-Based Decision Making in Education is a must read for researchers who are just beginning to explore the scientifically based nature of educational practice. It is also appropriate for policy makers and practitioners who are confronted with young people who need to be in classrooms where "best practices" are the norm and not the exception.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.