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In Academic Impressions’ recent edition of Higher Ed Impact: Monthly DIagnostic, “Success Leaves Clues: Predictive Modeling in Higher Education,” we interviewed a number of experts to provide a conceptual overview of how institutions can take steps toward a more rigorous mining of their current and historical student data to identify predictors not only of which students will be at-risk — but predictors of success. Learning the shared characteristics of persistent and academically successful students can drive more informed investments in your recruitment and enrollment efforts. This week, we wanted to isolate one of the “big” factors — GPA — and take a brief look at how you can take a more sophisticated look at high school GPA and first term GPA as predictors. For a few tips, we interviewed Bernadette Jungblut, West Virginia University’s director of assessment and retention, and Jim Scannell, president of Scannell & Kurz Inc. Here are some of the thoughts they shared with us. Rethinking High School GPA “Don’t just rely on overall high school GPA,” Jungblut warns, noting that often one’s first impulse is to isolate high school GPA as a more effective predictor than standardized test scores and other frequently-used indicators. But Jungblut suggests […]

With scarce resources, it’s time to become increasingly savvy about the students you recruit and enroll, how you support them in ways that make a difference in their academic success and persistence, and how you approach the student-to-alumni transition. Success Leaves Clues Look at your past and current students and your current donors to identify indicators of retention, academic success, giving, and engagement. This entails identifying the shared characteristics of those students and alumni who are most successful and have the most pronounced affinity for your institution. Success leaves clues, and locating those clues can help you make better decisions at each stage of the student lifecycle, from recruitment, to retention, to graduation and transition to alumni. Predictive modeling based on current student and alumni data can inform your investments in recruiting, student support, as well as student philanthropy and young alumni engagement. [[{“type”:”media”,”view_mode”:”media_large”,”fid”:”1405″,”attributes”:{“alt”:””,”border”:”0″,”class”:”media-image” ]] “Predictive modeling is a powerful statistical tool because it isolates those statistically significant variables that really drive the behavior of students—to find out of all the students who inquire, who will apply; or of admits, who will enroll; or of those enrolled, who will stay or who will leave.” Jim Scannell, President, Scannell & Kurz […]

Moving beyond high school GPA and standardized test scores, you will want to look for predictors of student success and affinity for your institution by analyzing data on your past and current students at each stage of their relationship with your institution. Identify shared characteristics of those students who model behaviors you want to encourage. Look for shared characteristics among: Prospects who apply (versus prospects who don’t) Admits who enroll Students who take leadership roles Students who perform high academically Students who persist into the second year Students who graduate within a certain time range Students who stay engaged with the institution after graduation and take leadership roles in the alumni community Once you know the characteristics of your current, most successful students, these characteristics become additional attributes that you can identify in your recruiting process. Suppose that your data indicates that a significant percentage of the students who show high academic performance and persistence in your engineering program took calculus prior to entering it—and you find that many of these are transfer students who completed calculus while enrolled at a local community college. With this knowledge, you can look to your applicant pool and identify applicants who have taken […]

Few institutions are performing sophisticated predictive modeling on factors affecting student retention, and given how many factors there are (both within and outside the institution’s control), predictive modeling can appear quite daunting. But you don’t have to go from A to Z all at once. “Typically,” Jim Scannell, president of Scannell & Kurz Inc, advises, “we encourage institutions not to leap into predictive modeling immediately. Start by doing univariate analysis, collecting descriptive knowledge.” For example, out of an entire class, you could set out to describe: How many men retain versus women? How many men versus women achieved higher than a 3.0 GPA? How did your transfer students from two-year institutions perform versus your transfer students from four-year institutions? Students from public versus private high schools? Student cohorts based on race? Suppose you find that men in that class are, on average, achieving a lower GPA than women. Can you dig deeper? For example, if you have strong athletics, compare both the academic preparation of entering athletes versus non-athletes and the academic success of those two groups during the first year. Do you have a lower GPA for male students because you enrolled 100 football players who were less academically […]

Your institution has enrolled a strong class of students, and a high percentage of them have persisted and are thriving academically. Now it is the senior year. In a few months, these students will graduate, and, if you do not engage them proactively now, you will lose your best opportunity to invite them to engage with you and give back to the institution as alumni. A few figures to consider: According to Eduventures’ 2008 study Transitioning Donors to Higher Gift Levels, almost half of all donors make their first gift to the institution more than 20 years before making a contribution at the major gift level. According to the 2011 Capgemini and Merrill Lynch World Wealth Report, 32%—nearly a third—of high net-worth individuals (HNWIs) in North America are under age 55, an increase in the number of the young wealthy over previous annual reports. These data speak to the importance of cultivating donors as early as possible. And as affinity with your institution develops while alumni are still students, managing the student-to-alumni transition is especially critical. Key Indicators of Giving and Engagement What can you learn about preparing students to become engaged alumni, based on the data you have (or […]

Marianne Pelletier, CFRE, the director of research and data support for Cornell University and author of the recent Academic Impressions monograph Prospecting Using Social Media, has embarked on some extensive experimentation with integrating social media into prospecting and prospect research. We interviewed her this week to ask why prospect researchers need to be moving on this quickly, and to ask what myths about social media and prospecting might stand in need of debunking. There are advancement shops that have made some early strides in this, and a few that have been using social media in prospecting for a while. This article is for those shops that are at the earliest stage or are considering it. GET A THOROUGH PRIMER ON USING SOCIAL MEDIA FOR PROSPECT RESEARCH This Academic Impressions monograph by Marianne Pelletier, CFRE, will walk you through more than 30 separate exercises in using social media for prospecting and for prospect research, and will provide you with introductory information on the following: The possibilities and limits of using social media in prospecting Tactics for mining social media to locate wealthy prospects, with walkthroughs of specific steps and examples How social media activity can reveal assets among your prospect pool […]

In a 2011 interview with Academic Impressions, Brad Ward, CEO of BlueFuego Inc., cited his organization’s research into the impact of university Facebook pages. After a 25-month study of nearly 400,000 Facebook updates across more than 1,200 university Facebook pages, Ward concluded that most institutions offer too much content via social media channels, leading to declines in engagement as their audiences begin to “tune them out.” Ward warns that quality is far more important than quantity, because institutions compete with family and friends for time and social media “space” — in short, for the attention of students and alumni on channels that are already overcrowded with content. It’s critical that marketing and communications and alumni relations offices invest more in listening to their audience’s social media preferences and preferred content. Effective listening can empower your office to offer highly targeted content — whether on your website, on your Facebook page, or via a mobile app. In a follow-up article in June 2011, we turned to Linda Thomas Brooks, president of Ingenuity Media, The Martin Group and past member of the board of directors for the Ohio State University Alumni Association. With considerable expertise in setting up effective social media listening posts, Brooks […]