For higher education, myriad challenges lie ahead. Amidst the uncertainties of a spiking COVID-19 pandemic, recent research from InsideTrack reveals college administrators are worried both about new enrollment (48%) and retention of existing student populations (80%).
They are right to be concerned; one in six high school seniors is rethinking the decision to attend college this fall. Many campuses have announced online learning for the fall semester, but educators worry that the decrease in face-to-face student interactions will negatively impact retention.
How can administrators improve enrollment and retention—and how can online data management bolster their efforts?
Responding to Crisis with Disruption
The COVID-19 pandemic in the United States has led academic institutions to cancel in-person classes in favor of online instruction. This new pedagogical model is just one example of how the virus has altered college life; the Big Ten and Pac-12 just postponed the fall sports season, which in turn has led to concerns about the financial future of many institutions of higher education: the University of Washington just announced the issuance of $78 million in refunds for student room and board.
- The latest surveys report 36% of colleges and universities are experiencing a decline in admission visits to campus.
- One survey showed that nearly 70% of high school seniors report their financial ability to attend college has been affected by the pandemic.
With every enrollment now more critical, educational institutions at all levels should rely more heavily on data analytics techniques to create actionable insight to fuel their student engagement efforts. The disruptive power of predictive and prescriptive analytics can ensure the success of the student population by helping faculty, staff and administrators gain a real-time understanding of populations while identifying at-risk students well before they decide to drop out.
Five Ways Predictive and Prescriptive Analytics Can Help Student Retention
Colleges and universities around the country have recognized that strategic alignment of data and enhanced analytics is key to developing processes to adapt to these challenges. These institutions have lakes of data waiting to be mined, and many have invested in cloud data management tools to systematize these resources to produce actionable insight.
With the right integration tools to connect diverse data sources and key methodology to apply predictive and prescriptive treatments, these institutions can benefit by tapping into data-driven insights to create personalized interventions to improve student retention. Today, these tools are being used to:
- Improve student engagement throughout their higher education experience.
- Personalize a highly interactive online learning experience.
- Create a student roadmap that sets academic and career-related goals to help guide students toward their degrees.
- Course-correct individual at-risk students struggling both academically and financially.
- Improve all of these efforts by creating a feedback loop between students and college administrators.
Instead of just picking classes, students and academic advisors can create personalized engagement plans that provide a clear view toward graduation goals and even employment. These same data analytics and visualization tools can be used to identify at-risk students, both from an academic and engagement perspective.
Colleges and universities must act now to capitalize on the data available to retain students. In this current climate of crisis and uncertainty, every student enrollment matters to these struggling higher educational institutions. A unified approach to integration and enhanced analytic management can lead to well-timed interventions that can use predictive analytics to proactively improve student retention outcomes through the 2021 academic season—and beyond.
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Deepinder Uppal is the vice president for Innovation and Technology in the Public Sector for ibi. In this role, Deepinder helps to define a customer’s technical vision by assisting with the successful creation, integration and deployment of next-generation analytics and data architecture. Deepinder has a Master’s in Advanced Logic and his Doctoral dissertation and subsequent research is in the Bayesian application of Containment. Deepinder has innovated on remote course content enrichment data models, AI-driven course scheduling methodologies, adaptive course content in online courses, and predictive and prescriptive capabilities at numerous academic institutions. Deepinder has presented his research at academic conferences, held workshops at major research institutions, and has been the recipient of numerous grants and awards. Outside of academics, Deepinder is a United States Army veteran and has served honorably in numerous areas of conflict.