This article is part of NASFAA's occasional book review series, where members share their reflections on books, published within the past five years, on higher education themes of interest to financial aid professionals. The opinions offered and statements made do not imply endorsement by NASFAA or the authors' employers and do not guarantee the accuracy of information presented. Would you like to suggest a book for a future review? Email us at [email protected] with your recommendation.
Although financial aid professionals have long been awash in data, our historical relationship to data has been largely transactional. We review and update student data received from multiple sources, use data to make awarding decisions, transmit data to the U.S. Department of Education, and use data to comply with reporting requirements. However, a relatively new and sometimes overwhelming change to the responsibilities of financial aid administrators is that we are expected to harness the potential power of big data to understand student behavior, identify students who may be at risk of attrition due to financial challenges, and develop tuition discounting strategies to maximize enrollment and net tuition revenue. Financial aid professionals seeking a foundational understanding of big data in postsecondary education will benefit from reading “Big Data on Campus: Data Analytics and Decision Making in Higher Education.”
In this book, edited by Karen L. Webber and Henry Y. Zheng, experts on big data in higher education explore best practices for managing big data, highlight several examples of how institutions have used big data to make meaningful improvements to student and institutional outcomes, and offer some ethical considerations related to the use of big data. The unifying theme of the book is its emphasis on the essential subjective judgment of professionals to interpret data to drive progress toward their institutions’ goals. Big data is one of many tools professionals use to inform their decision-making, and the authors caution throughout the book that merely gathering more data and deploying sophisticated technology cannot solve an institution’s woes.
A significant strength of the book is that it defines terms related to big data in plain language. For example, rather than assuming the reader is familiar with the terminology, Chapter 1 provides definitions and explanations of data-driven decision-making (“let data speak for itself”) and data-informed decision-making (“let us figure out what data tells us”) (p. 6). Throughout the book, the authors express a strong preference for the latter. Chapter 2 defines big data as a “broad term that includes huge amounts of structured data (such as all the clicks of all students in all online learning materials at a university), but also unstructured data like social media feeds with text, images, and video files, as well as a set of non-hypothesis-driven analytical techniques applied to existing (smaller) data sets” (p. 31). Chapter 3 is similarly helpful in understanding how descriptive analytics models (“designed to give a better understanding of individuals or their behaviors”) and predictive analytics models (“designed to anticipate certain outcomes or responses”) differ (p. 54).
Financial aid professionals invited to participate in data governance committees would be well served by reviewing Chapter 6, as it clarifies the purpose of such committees by defining data governance, summarizing common institutional data governance goals, and making a compelling case for building trust in institutional data by encouraging campus-wide involvement in data governance.
The book features several examples of how committed professionals successfully used big data to inform their decisions and make meaningful changes. A turnaround story from Georgia State University (GSU) in Chapter 8 is particularly powerful. Financial aid professionals will appreciate how GSU used predictive analytics to provide better advising to students and to award microgrants to students at risk of attrition due to outstanding balances. The author of this chapter, Timothy Resnick, senior vice president of student success and professor at GSU, emphasizes that the university achieved this turnaround by making better use of the data they already had rather than collecting additional data from students. Institutions that are just getting started with the use of big data may be concerned that they will get it wrong because they have flawed or incomplete data. Resnick provides helpful advice for such institutions when he explains, “The data are never perfect — but even imperfect data can provide the basis for improved decisions if current judgments are, in effect, based on no data at all” (p. 195).
The ethical use of big data is a frequent news topic and many significant scandals have emerged in recent years, including the Facebook Cambridge Analytica scandal and potential consumer data harvesting through the popular foreign-owned app TikTok. Although the book discusses some ethical considerations related to big data, it does not thoroughly address a key institutional use of big data that is of concern to many financial aid professionals, which is the practice of tuition discounting models developed through price sensitivity analysis and sophisticated predictive algorithms. Chapter 7 of the book, which discusses how data analytics inform decision-making in enrollment management, briefly mentions that the current focus on enrolling and retaining Federal Pell-eligible students results in underserving other high-need students who just missed the cutoff for Pell Grant eligibility. However, it does not discuss how the goal of tuition discounting at many institutions (maximizing net revenue and enrollment) requires institutions to use student data to predict the smallest discount they can offer to individual students to convince them to enroll. Such approaches can result in first-generation and low-income students receiving lesser financial aid offers than wealthier students, in conflict with many institutions’ stated diversity, equity, and inclusion (DEI) goals. Financial aid administrators and other enrollment management professionals struggle with tuition discounting, which requires them to balance the financial needs of the institution with their personal commitment to equity and the institution’s stated DEI goals. It is a missed opportunity that the book does not explore this topic in more detail, especially considering the copious popular media coverage of college costs and equity.
Although there is a narrative at this time that advanced artificial intelligence, like ChatGPT, is superior to human intelligence and will replace many professions, this book makes a compelling argument that human interaction with and interpretation of big data is essential to giving it meaning. As contributor Braden Hosch warns, "Even when the garbage-in, garbage-out problem is overcome, institutional leaders should not be naïve enough to think that cutting a check to a technology company will substitute for the hard work of organizational transformation” (p. 45). Financial aid professionals seeking a foundational understanding of the terms, controversies, and promises of using big data to serve their institutions and students will find this book a helpful starting point.
Cynthia Grunden is a financial aid and higher education specialist at Powers Pyles Sutter & Verville PC. Prior to her current role, Cynthia worked in higher education for 20 years. She earned an Ed.D. in educational leadership and organizational change from Roosevelt University, and a master's degree in higher education administration and bachelor's degree in secondary education from Indiana University. Cynthia earned the SEM Endorsement Badge through the American Association of Collegiate Registrars and Admissions Officers and the FAAC® designation through NASFAA, and works as an independent consultant for Blue Icon Advisors.
Publication Date: 6/6/2023