When I was an education consultant in Silicon Valley five years ago, Palo Alto was experiencing a rare phenomenon: two suicide clusters within the span of a few years. To put things in perspective: the U.S. usually experiences five clusters per year, which are defined as multiple suicides in a short time frame. What’s more, in Palo Alto nearly all of the victims were teenagers; two of the highest performing schools in the area, for example, Gunn and Palo Alto High, have 10-year suicide rates between four and five times the national average. The Caltrain (the Bay Area’s commuter rail) became synonymous with suicide, so much so that it garnered the attention of the Centers for Disease Control and Prevention, which launched an epidemiological study in the area in 2016.
Meanwhile, I was consulting students ages 12-18 at and around these schools, for a reputable contender in the rapidly growing industry of college admissions private consulting. The San Francisco Bay Area, where I was working, has the highest concentration per capita of these consulting agencies, contributing to an industry that has grown exponentially in the past few years (more than a quarter of college applicants in the top 70th SAT percentile used a college consultant in 2015). The industry is worth over $400 million, and tuition for these private programs can rival that of a community college (plus, personal consultants can freelance for up to $400 or more per hour).
Why do these families insist on using consultants? Well, perhaps what I should be asking is why there have been so many teenage suicides in Silicon Valley. The college admissions world is high intensity and high pressure; consultants are trained to be college admissions experts in standardized testing, summer internship programs (many that have lower acceptance rates than Stanford itself), course selection, college major requirements, application essays…the list goes on.
This industry has emerged from the reality that getting into college is more cutthroat than it has ever been before. While overall application rates are decreasing, “brand name” schools are at a loss from their own popularity; Stanford, for instance, received over 44,000 applications for just over 1,700 spots last year. Plus, strapped for cash, more colleges and universities are seeking out-of-state or international students to pay full tuition, particularly at state schools. With seemingly high stakes, many students report anxiety and depression due to academic pressures, and students, parents, and administrators alike argue that the admissions process is broken.
In response, colleges have been turning to technology to get a more holistic view of applicants— and social media is just the start. One of my favorite parts of consulting was Googling and Facebook-stalking my students to make sure their profiles were college-proof (trying to politely and professionally tell them that they should remove the “FADED” and “DGAF” captions from their photos was another story), but it’s not just me. About 40% of college admissions officers admit to researching applicants’ social media profiles, a number that has quadrupled since 2008. Most are doing so not only to verify details around application questions, but also to better understand the applicant’s personality or leadership style; many report finding details that actually enhance a student’s application. Students, too, are appealing to universities with multimedia applications, created through tools such as the Coalition for Access, Affordability, and Success’ digital locker, or ZeeMee’s online profile creator, which allow counselors to get to know applicants in different ways. Students can even “tour” schools using virtual reality.
In addition, institutions such as Ithaca College are using big data programs designed by IBM to identify the kinds of students most likely to graduate, and schools such as Wichita State use algorithms to quantify student interest, as well as potential return, then allot recruiting money based on probability of enrollment. And after those students are admitted, the schools are using predictive analytics to determine which individuals may need more academic support to succeed. In 2011, for instance, Georgia State was one of the first major universities to use ten years worth of academic and attendance data to create a model that flagged students at risk of making decisions that could lead to dropout—an approach that has saved 2016 graduates $15 million total compared to the Class of 2012.
The use of data as it relates to student interest, ability to succeed, and fit may seem invasive or even highly error-prone, but schools view it as a smart use of resources in a high stakes scenario. After all, student retention rates hover around 60% overall, and almost half of full-time, four-year students graduate in six years (rates that are even lower for black and hispanic students). Universities are constantly striving to improve these statistics, as high attrition rates can mean disrupted learning and living experiences for students. What’s more—and arguably most important with institutions of higher education inextricably tied to financial interests, a situation that could become more dire with recent tax proposals—is attrition means lost revenue for universities. At Georgia State, for example, a 1 percent increase in retention rate means an additional $3 million in return on investment.
However, there are many arguments against the use of big data to impact admissions decisions; as CTO of Dell EMC’s big data Bill Schmarzo argues, universities could easily use big data to tailor their admissions practices, as well as curriculum and services, to students more likely to become big donors after graduation (even if some of the nation’s most selective institutions already enroll more students from the top 1 percent of the income ladder than from the bottom 60 percent). While he claims that this would allow schools to recommend “better fitting” schools to students denied admission, this thinking could reinforce discriminatory stereotypes, as recent data suggests that students technically “under-qualified” but with above average test scores will perform better at more selective colleges.
Also, as with any data, there are of course privacy concerns, and what’s more, the implementation and maintenance of these programs can be costly, as well as difficult to execute flawlessly. How, for example, do you measure a student’s potential for growth in an unfamiliar environment, given limited information about the context of their existing data? How do you judge students with little to no social media presence? Furthermore, beyond admissions, communicating to “at risk” students, if not done well, can actually end up alienating or demotivating students further, so schools must be careful about who gets access to this data.
Transparency around colleges’ use of data analytics is also an interesting predicament. On the one hand, it can help students who know how to “play the game” with schools—and this, in full transparency, was the focus of much of our consulting. If students can increase their chances of acceptance just by watching a few YouTube videos or webinars, or even talking to a chatbot, why wouldn’t they? Researchers, however, argue that the use of technology could tilt the odds in favor of students who already have increased access to college, contributing to the racial and socioeconomic divide in college admissions.
Plus, many worry that colleges are putting so much pressure on applicants that students arrive on campus underprepared for their college experiences. Experts argue that students spend more time focused on college applications than actual, impactful learning in high school. And of course, as seen in the Palo Alto tragedies, there’s the stress students report as a result of the intense pressure they feel to “compete” in college admissions—pressure coming from their families, their peers, their schools—and even, the colleges themselves. Therefore, while it’s undeniable that technology has the power to upend the admissions process and its potentially harmful effects, the onus is on the schools to define what technology they need, how they should use it (ie, knowing both its strengths and failings), and most importantly, how it supports their missions as institutions of higher learning.