ETHICS AND STANDARDS ARE A FIRST PRIORITY OF ANY STUDENT DATA ENDEAVOR
Institutions of higher learning must always make safeguarding student data a top priority. Today’s students are very willing to share information with organizations for a variety of services deemed essential in the digital age. Information is freely given and constantly updated when gaining access to online social networks, using a smart phone, and registering for e-mail. In all cases, students must be made aware of their rights and, if data are used, how they could assist them in receiving services and making more informed choices.
Any discussion of deep data mining and uses of personal information results in visions of “Big Brother” tracking technologies and the obvious potential for innocent people to be harmed. Although the authors assume any use of Big Data in higher education and enrollment management will be carried out with the strongest ethical considerations and in strict compliance with federal and state laws, we are not naïve concerning the abuses likely to happen when data conclusions are misunderstood or the conclusive applications or policies potentially violate civil rights for the sake of efficiency.
Many laws exist to protect student privacy, including the Family Educational Rights and Privacy Act (FERPA). Failure to comply with the regulations could harm students personally and financially, plus lead to severe institutional financial penalties. However, the possibility of using Big Data to find and recruit a student with a high probability for success is not only a noble activity, it is also a relatively new capability and expectation. The data protection and enforcement mechanisms must have many self-regulated, stop-gap measures built into the storage and sharing processes. Any of these new capabilities will require an investment of time to ensure safeguards are followed to the fullest extent.
STRONG ENROLLMENT MANAGEMENT HAS ALWAYS BEEN DATA DRIVEN
“To the organized, go the students.” —Jack Maguire
Today’s college admissions and retention programs rely on deftly organized and personalized multimedia campaigns (e.g., direct mail, telephone, e-mail, internet homepages, mass media, social media, and mobile media). The institutional activities and expenditures supporting these efforts can be extensive. A recent study estimated private colleges and universities spend an average of $2,433 for every matriculating freshman, while public four-year institutions invest about $457 per new student enrolled (Noel-Levitz, 2013). The methods for effectively managing these activities have significantly developed over the past four decades.
The enrollment management (EM) concept formally materialized in the 1970s among private colleges and universities seeking to have stronger, more sustainable student enrollment cycles. Jack Maguire and his colleagues at Boston College are largely credited with developing constructs and systems that now help colleges attract and enroll their desired quantity, quality, and diversity of students (Black, 2001, p. 6). In 1976, Maguire’s team recognized the need to have data guide the core enrollment management components. They categorized the five components as: “marketing admissions; research and information flow; market prediction; financial aid strategies; and retention and transfer programs” (Epstein, 2010, p. 4).
As access to richer data increased, so did enrollment-related in-formation and predictive programs. Analytic and statistical models have been developed to predict students’ interest levels, travel and mobility ranges, likelihood for successfully passing courses, financial need levels, and likelihood of persisting to graduation. Hoover (2011) stressed the heavy emphasis on today’s admission and enrollment leaders to be statistically competent when he noted: “A profession (college admissions) that once relied on anecdotes and descriptive data now runs on complex statistical analyses and market research. Knowing how to decipher enrollment outcomes is a given; knowing how to forecast the future is a must” (Hoover, 2011).
Today’s typical admission application information, secondary school transcripts, and college enrollment patterns can provide EM leaders and institutional researchers with hundreds of unique and comparable data points. The ACT college entrance/placement exam alone can provided over 265 data fields through detailed cognitive student demographic (age, gender, family income, etc.) and psycho-graphic (lifestyle activities) information (ACT, 2013).
Coupled with institutional performance trends, college admissions exam data offer insights to students’ academic development needs, college selection preferences, and career aspirations. The test and profile data can also indicate students’ aptitude to succeed in first-year classes and thus the likelihood to fit in or match well with the academic rigor of a particular school. A number of studies have established the strong correlation between higher ACT or SAT composite scores and positive college persistence levels (Burton & Ra-mist, 2001; Bettinger, Evans, & Pope, 2013).
Excerpt courtesy of State University of New York Press. Reprinted by permission from Building a Smarter University: Big Data, Innovation, and Analytics by Jason E. Lane, the State University of New York Press ©2014, State University of New York. All rights reserved.
A complete list of citations can be found in the book.
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.