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The Importance of Predictive Analytics in Higher Education

In this special guest feature, Brian Rowe, founder & CEO of Perceivant, explains how predictive analytics has evolved into a hot button topic among educators in order to better serve students by becoming more data-informed. This is a result of the intense pressure placed on universities to demonstrate an ROI for students as the U.S. dropout rate continues to be at an all-time high. Perceivant publishes and provides courseware that replaces traditional textbooks with cost-effective and interactive learning experiences for both web and mobile applications. Each course is accompanied by powerful analytics and real-time data to boost student engagement and provide educators with an easier, more efficient solution to analyze course efficacy.

Predictive analytics has evolved into a hot button topic among educators in order to better serve students by becoming more data-informed. This is a result of the intense pressure placed on universities to demonstrate an ROI for students as the U.S. dropout rate continues to be at an all-time high.

However, new reports revealed that less than half of higher education institutions are effectively engaging in predictive analytics. This proves that universities need to be more informed about how executing predictive analytics correctly and ethically can resolve many industry challenges.

One of the main solutions of predictive analytics is adapting and personalizing the learning experience in a more efficient way.

By leveraging predictive analytics, educators can identify patterns of students’ learning deficits and customize the academic experience so they are aligned to learn. It also can help students accelerate their learning by allowing them to move quickly through content they already know and provide them with additional support in areas they have not mastered. Therefore, the data insights and analysis will help change the conversation for educators regarding how to think about students’ progress and sustain success.

While this practice can be leveraged at all collegiate levels, it’s vital it be implemented during the more vulnerable years for students. For instance, the latest trends have shown that only half of all students will leave college with a diploma. And the biggest drop out rate occurs after a student’s first year, with some universities fighting a freshman retention rates as low as 47 percent.

Therefore, universities need to be better equipped to engage students during this time period without compromising academic integrity. Doing so will help ensure students are not deflected while boosting a higher likelihood of retention.

Take Kennesaw State University (KSU), for instance. They implemented this practice in a recent general curriculum course, which is typically taken by first-year students, and experienced amazing results.

By using predictive analytics alongside innovative courseware with adaptive learning practices, KSU was able to monitor student engagement and performance in real-time to identify trends of successful students and better understand the efficacy of the course. In addition, professors were able to better identify students at risk of failing or not completing the class and help improve student success earlier with higher impact.

Furthermore, this data also allows professors to understand the engagement and class comprehension of its content, assignments, course design and more. This feedback is incredibly valuable to educators as well as course authors, so they can make adjustments to the course and better understand its efficacy without compromising its integrity.

As a result, KSU was able to decrease fail and dropout rates by 50 percent. Student success has also improved, too. GPAs were better and engagement was at an all-time high.

In addition, by authoring half of content in the course, KSU proved how content can help improve learning outcomes.

While this is only one example of how predictive analytics can improve learning, it highlights the overall effectiveness of its uses in classrooms to boost retention and establish new baselines of engagement. And with only a small fraction of colleges and universities leveraging predictive analytics in the classroom, it is more vital than ever for institutions to understand this practice. Doing so will allow colleges to reap numerous benefits that that will significantly reshape the future of higher education.

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Comments

  1. Philip Batty says:

    “the U.S. dropout rate continues to be at an all-time high.”

    What’s your evidence for this?

  2. Thanks for sharing the Importance of Predictive Analytics in Higher Education. The course study is accompanied by real-time data to boost student engagement and provide educators with an easier, more efficient solution to analyze course efficacy. Predictive analytics has a chance to evolve into a hot button topic among educators in order to better serve students by becoming more data-informed. One of the main solutions to predictive analytics is adapting and personalizing the learning experience in a more efficient way.

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