Higher education has faced the same challenges for the past decade. Issues of inequity, affordability, and declining enrollment stem from flaws in the traditional higher ed foundation that ceased being relevant long ago. Developing a strong understanding of student experience analytics in higher education is necessary to build better outcomes.
An educated public is a society’s greatest asset, and the majority of positions the country clamors to fill require a college degree. Even with this evidence, the public remains skeptical about the value of an education that many must go deeply in debt to obtain.
To do the most good for the most people, institutions must reevaluate long-standing assumptions, re-imagine how they can best serve the public, and develop a new relationship with technology to make better, data-driven decisions in higher education.
Analytics for Student Success
Every student’s experience impacts their university as a whole. This statement isn’t some feel-good recruitment tactic; it’s data science. Advanced student experience analytics show exactly how much and in what ways a positive or negative student experience affects recruiting, enrollment, career placement, advocacy, and ultimately, the very resources universities use to serve and support their students.
Institutions must invest both money and time in talent and tools to use data in new, more impactful ways that build positive outcomes. Data silos languishing in legacy systems in separate departments aren’t going to get you there. Leadership must publicly elevate analytics to a strategic priority and make data-driven decisions.
People-Focused, Data-Driven Decisions in Higher Education
The world at large has undergone a digital transformation. From Massive Open Online Courses (MOOCs) at companies like edX, Khan Academy, and Udemy to YouTube tutorials on everything under the sun, it’s clear that universities no longer have a monopoly on education, only on degrees—and even that is changing. A student’s experience is only partially about information exchange. So how will colleges stay relevant in this new educational landscape? By getting very clear about their purpose, who they are serving, and what those people need.
Universities that thrive going forward will look outward to the world around them to fulfill needs—needs that data analytics clarifies. 2020 made us all face the absolute necessity of having a healthy relationship with technology. Some institutions pivoted quickly to online learning channels when schools shut down; others had a much slower integration. Obviously some courses adapt better to online instruction—science labs, for instance, are challenging.
But with normal learning paths closed, education had to continue. Post-quarantine analysis reveals that some classes are more effective online, as are some students. Removing geographic location barriers with virtual offerings makes more students reachable. Valuable insights from data analysis fuel data-driven decisions that bring people-focused, transformational results.
Student Experience Analytics: From Snapshots to Big Picture
Recruiting students and supporting them toward their greatest academic, career, and life success depends upon developing a full view that includes not just academic performance but extracurricular activities, health and well-being, social engagement, and a range of other factors—including ones that may be specific to your institution. Student experience analytics help you develop this complex picture.
Data-driven decisions make the difference between students who leave before degree completion and graduates who become full-throated advocates.
Data Analysis for Effective Recruiting
Traditionally, colleges spend big money for high school student names from companies that administer standardized testing. At nearly fifty cents each, this broad net with a low conversion rate doesn’t have an ideal ROI.
Advanced analytics can make clear what criteria indicate a good fit between a student and a school: the distance they’re willing to go from home, intended major, outside interests, extracurricular activities. Colleges can get specific on which names they buy, lowering initial costs.
This data also leads to discoveries about your true competitors, which geographic locations yield the best results, and how to better distribute scholarships and financial aid, leading to greater retention.
Big Data Brings Big Responsibility
Using data analysis to make data-driven decisions in higher education gives universities tremendous power, but with caution. While stats themselves are without bias, they reflect the current state of The Way Things Are. Intent and motive behind the analysis is the difference between addressing old, ingrained assumptions or simply justifying them.
Creating sustainable change depends upon contextual consideration and deep understanding of the issues you’re trying to solve before implementing new solutions. Expansive mindset changes and reprioritization will make higher education more accessible and avoid reinforcing the current system that works well for some and leaves others at a disadvantage.
Eliminating Bias in College Recruiting
To build a more diverse student body, scrutinize past criteria for admission decisions; there’s likely inherent bias there. Broadening those criteria to include context helps illuminate what is unique about an individual student’s achievements—academic, social, entrepreneurial, philanthropic—for their hometown, home situation, or income level.
Nontraditional students—those who wait to start college, work full-time, care for their children or parents, and transfer students—make up the majority of people furthering their education, yet most institutions fixate on traditional students for policies, procedures, the way they teach, and recruiting. Universities that thrive recognize this and act accordingly.
Retention, Retention, Retention
Enrolling more appropriately matched students automatically increases the likelihood that they’ll stay through degree completion, but student experience analytics can improve retention in other ways.
Personality tests and other indicators can reveal best learning styles and environments so schools can reach more students where they are. Predictive analysis can identify students with a higher risk of dropping out so professors and advisors can preemptively redirect them through assistance with study habits, different ways of learning, or changing majors. Analytics can zero in on non-academic reasons a student may want to leave, leading to solutions for easier day-to-day living, new services, or different environments.
Affordability Equals Sustainability
The current model of higher education is unaffordable for many people and unsustainable. Thinking outside their usual parameters, institutions can use data analytics to make data-driven decisions in higher education operations:
Universities Thrive When They Have the Tools to Maximize Student Experience
There’s no single answer for improving the student experience. It begins with leadership creating the culture that values and respects data. Investing time to find the right talent and technology that will fuel data analytics is essential. But having data from student experience analytics is one thing; mining it for insights and acting upon those insights is quite another. Using these insights to make data-driven decisions in higher education is how universities will build desired outcomes.