Surveying the Role of Data in Higher Education

Big Data on Campus: Data Analytics and Decision Making in Higher Education ed. Karen L. Webber and Henry Y. Zhang. Baltimore: Johns Hopkins University Press, 2020.

For many years, I taught a doctoral-level course on college students in higher education; one assigned reading was Marc Parry’s 2012 Chronicle of Higher Education article “College Degrees, Designed by the Numbers.” Parry reported on the practical realities of data software systems that monitor student academic progress and emerging social media platforms that guide students’ social lives on campus. This article and ensuing classroom discussions helped to orient my class of current and future higher education faculty members and administrators to the changing practices and increasing “datafication” of student affairs and academic affairs programs and services.

Almost ten years later, Karen L. Webber and Henry Y. Zheng’s 2020 edited essay collection Big Data on Campus takes a more expansive approach with perspectives on institutional research; data analytics for academic leaders, student affairs processes, and student learning; and institutional financial management. As a result, the primary audiences for this book are senior administrative leaders, institutional research and institutional effectiveness professionals, information technology staff, and graduate students who study higher education.

The chapters in this book address critically important topics related to data use in higher education, including data ethics and privacy issues in the discussions of some data-collection practices, such as using wireless node tracking of students’ mobile devices on campus to explore relationships between class attendance and academic success. The book begins with a useful differentiation of the terms data-informed and data-driven decision-making: “Whereas data-driven decision making (DDDM) lets the data speak for itself, data-informed decision making (DIDM) considers the context in which the decision is being made. We believe that analytic results are best when the information is examined within the specific situation or context, and that means that in some cases, the algorithmically generated answer or prediction with the highest accuracy rate may not be the best answer for the situation at hand.” Novice researchers who use quantitative data analyses can be lulled into the belief that, after selecting variables and methods of quantitative analysis in statistical software such as SPSS, SAS, or R, clicking the “run” button transfers all subsequent academic decisionmaking to the software with little additional effort necessary. Webber and Zheng’s discussion of the terms DDM and DIDM enables readers to gain a clear understanding of the importance of institutional context when employing data analytics in the decision-making process and of the responsibilities of administrators and practitioners to apply their specific knowledge of campus conditions when interpreting data.

After the November 2022 emergence of ChatGPT, Braden J. Hosch’s contribution to the collection, “Big Data and the Transformation of Decision Making in Higher Education,” seems prescient in exploring future applications of artificial intelligence (AI) in institutional planning. We can now answer yes to some of the questions raised in this chapter, including, for example, “Can the decisions that a financial aid counselor make[s] be replaced by AI with perhaps a summary review from an associate director or director level position?” Practitioners and students in health-professions education are already using ChatGPT to write patient-treatment plans, and faculty and students in teacher education programs are using ChatGPT to write lesson plans. These are just two of the myriad applications of AI in higher education.

One notable strength of this book is the inclusion of case studies to provide examples of specific practices that have been implemented at individual institutions; readers interested in pragmatic aspects of data analytics will find these descriptions useful. However, rapid advances in technological applications—combined with changes to internal, external, and organizational conditions resulting from the COVID-19 pandemic—may make these descriptions and examples less relevant to their respective institutional situations.

Nathan Grawe has argued in his books Demographics and the Demand for Higher Education and The Agile College, the latter reviewed elsewhere in this issue of Academe, that an approaching “demographic cliff” of the prospective college-going student population threatens to undermine future demand for higher education. He predicts that groups of peer institutions may need to use increasing amounts of resources and intensify recruitment efforts to attract students from shrinking pools of qualified applicants. Reflecting this imperative, Tom Gutman and Brian P. Hinote’s essay in Big Data on Campus, “Data Analytics and Decision Making in Admissions and Enrollment Management,” uses effective data visualizations to make an important contribution to the intersection of predictive data analytics and strategic decision-making in institutional planning for future demographic challenges.

I served for twenty years on the graduate faculty at a chronically resource-poor regional public university. In reading the contributions to Webber and Zheng’s collection, I noted frequent examples of applications at Research I institutions of sophisticated customer-relationship management (CRM) software systems that analyze data to inform institutional planning on factors such as social media engagement, return on investment, admissions yields, and alumni and employer relations. These expensive systems and the consulting companies that are hired to guide their selection and implementation exemplify what’s possible at well-resourced, research-oriented institutions and the competitive advantages they enjoy in the admissions arms race. These systems are often unaffordable for less-selective, teaching-oriented institutions without significant external research-grant activity or other revenue sources, which constrains their abilities to be successful in the face of future demographic and economic challenges. As a result, readers employed at resource-poor institutions may find less value in the book’s discussions of technology-based strategies and solutions that may be beyond their institutions’ reach.

Some chapters briefly mention individual community colleges in broader discussions or focus in depth on a minority-serving institution, Georgia State University. I hope there will be a second edition of this book that provides technological updates on the future possibilities of implementing artificial intelligence in the era of ChatGPT and on predictive data analytics for institutional planning. Such an update should include chapters devoted specifically to other important institutional types (for example, community colleges, historically Black colleges and universities, Hispanic-serving institutions, tribal colleges, and less-selective private liberal arts colleges) and to resource-poor institutions. By focusing on a wider range of colleges and universities, the next edition would assist administrators and institutional research practitioners who serve the increasingly diverse college-going population in the future. 

Jim Vander Putten is associate professor of higher education at Mercer University. His email address is [email protected].