How predictive analytics are shaping the future of higher education
chief executive officer, Perceivant
With the U.S. dropout rate at 45 percent, student debt at $1 trillion and student depression at an all-time high, colleges are under intense scrutiny to demonstrate an ROI for students. As a result of these pressures, institutions have begun aggregating data to determine various strategies that predict outcomes of current and prospective students.
Enter predictive analytics, which has been flooding into various sectors of business operations at colleges nationwide. By using it accurately, universities can better tailor advising services, personalize learning to improve student outcomes and establish more segmented recruiting efforts, among other initiatives.
While these efforts are worthwhile, it is crucial to first understand how to use predictive analytics ethically to ensure it’s not curtailing academic success. Failing to comprehend the practice invokes many concerns as to whether the benefits outweigh the heightened risk of the data being leveraged way outside the original intended purpose.
For instance, an institution could leverage this consolidation of data to justify using fewer resources to recruit low-income students because they’re less guaranteed to enroll and graduate than more affluent prospects. Therefore, studies have revealed that the best predictive analytics need to reinforce a sense of equality. Adhering to this approach will help better lead students throughout their college lifecycle from prospect to graduate.
While leveraging predictive analytics does come with an intense level of training, data security and ethical understandings, college can reap numerous benefits that will significantly reshape the future of higher education.
1. Promotes personalized learning to improve student outcomes.
Personalizing learning based on the needs of each student is a practice that has proven to be successful. However, this method also brings a series of instructional challenges that are impossible to manage without intelligent technology. That’s why data and predictive analytics can play a pivotal role in developing more personalized learning strategies that boost student engagement and improve student outcomes.
By leveraging predictive analytics, educators can identify patterns of disengagement and intervene earlier with higher impact. This practice also helps ensure students receive the right instruction, intervention and challenges to effectively achieve learning goals. Therefore, the data insights and analysis will help change the conversation for educators regarding how to think about students’ progress and sustain success.
Since this practice is so novel, there is a significant shortage of professionals with the necessary expertise to actually mine and interpret the data. That’s why it’s important for universities to seek out experts or partners who can handle this practice ethically and effectively for the betterment of students.
2. Helps understand the efficacy of courses to boost student engagement.
Based on the latest trends, only half of all students will leave college with a diploma. And colleges nationwide are noticing the biggest drop out rate occurs after a student’s first or second year when schedules are typically overpowered with more general education classes. Therefore, universities need to be better equipped to engage students during this time period without compromising the course’s integrity. Doing so will help ensure students are not deflected while boosting a higher likelihood of retention.
To overcome this battle, universities are levering innovative web-based courseware that are filled with date-driven analytics to help analyze the efficacy of courses to boost student engagement. With the courseware, educators are able to track student engagement in real-time and determine which strategies drive the highest level of involvement.
Recently, Kennesaw State University (KSU) implemented this approach in its WELL 1000 course, which is a required core curriculum class designed to teach priority health issues through a focus on health promotion and disease prevention. By using this web-based courseware that helped measure its efficacy, KSU experienced amazing results. The course’s DFWI (D, fail, withdraw, incomplete) rate were reduced by 48 percent. Most notably, the online section of the course saw a 55 percent reduction over the same time period.
This is just one example of how predictive analytics integrated within more accessible and interactive courseware is able to effectively establish new baselines of student engagement while better determining the overall effectiveness of general education.
3. Tailors advising services to meet struggling students.
Students today are more likely than ever before to visit a college advisor, which is largely due to an incredible increase in non-traditional students who deal with situations that can make attending college full-time difficult. This rise is also making it even more problematic for advisors and students to maintain successful relationships.
According to a recent survey the University of Texas at Austin, 78 percent of respondents reported to have met with an advisor, but experienced varying levels of success given the lack of resources available to full-time academic advisors whose average caseload is 300-to-one. This has created a greater demand from advisors for more user-friendly tools that distribute actionable insights to help student overcome barriers of achievement before it’s too late.
Leveraging predictive analytics can help. It allows advisors to be alerted if a student is veering off path for graduation in the desired major, allowing for necessary intervention to be executed. By leveraging this technology, institutions will see an increase in retention and graduation through intensive advisement on the basis of early alerts.
4. Establishes better enrollment management.
The cost of recruiting and educating students is continuing to rise, making retention even more crucial to a university’s bottom line. As costs increase, colleges hope to overcome this challenge by balancing the resources used to recruit students with revenue generated when those students are retained.
With predictive analytics, schools can better inform enrollment management plans and forecast the size of incoming and returning classes. It can also be used to help the school narrow the focus of their recruitment and marketing efforts so they are ethically targeting students who are most likely to apply, enroll and succeed. Even with small improvements to the yield rate predictability and student retention numbers, universities can successfully create better learning outcomes, operational efficiency and funding decisions.
Predictive analytics: The future of higher education
Predictive analytics unlock the key to a new future in education. It allows universities to help students map their progress toward a degree, determine their academic pathway and improve skills to enhance their education. And ignoring these technological opportunities that complement our human capacities may lead to a disservice to students. For those institutions who boast the necessary resources to leverage predictive analytics in the right way, now is the time to unleash these capabilities to drive a more collaborative, student-centric experience. Doing so can create a better learning future.