A Peek into the Future: Predictive Analytics in Education
August 03, 2021
In 2015, the management of the Kennesaw State University in Georgia (KSU) decided to redesign its Foundations for Healthy Living course (WELL 1000). The aim was to address common pain points that haunt many educational institutions, such as unsatisfactory grades and the drop-out ratio, reflected in a high DFWI (D, fail, withdraw, incomplete) rate.
Over just two years, the KSU managed to reduce this key metric for the WELL 1000 course by 48%, from 25% to 13%. To this end, the university used various methods, including tailored curriculums, online learning platform integration, and providing an overall more interactive experience for students.
All these solutions had one thing in common: they were suggested by predictive analytics. The success story of the KSU goes to show that the data-driven approach, adopted years ago by tech giants like Amazon and Netflix, has a place in education, too.
In this article, we take a closer look at the current and potential applications of predictive analytics in learning. Read on to learn how you can harness data in your educational facility and discover best practices that will guarantee a smooth transition into the world of advanced analytics.
What Is Predictive Analytics?
Predictive analytics is a broad term that refers to machine learning algorithms that make informed conclusions based on high volumes of data. In other words, it’s how we apply modern technology to take a look into the future and improve the way we work, live and learn.
Predictive analytics is now more popular than ever, even though the concept has been around for quite some time. The main drivers for the adoption of predictive analytics software include more affordable computing, greater internet coverage, cloud computing expansion, and facilitated access to ever-growing amounts of data.
Powering Smart Decisions
Data-driven decisions have become part and parcel of modern organizations in all verticals, including education. In its essence, predictive analytics helps unearth business insights that nurture data-based decision-making. Here are some examples:
- In online retail, predictive analytics considers historical data to target priority customers and analyze their on-site behavior.
- When applied in healthcare, predictive analytics solutions facilitate risk assessment and make it easier to deliver treatment adjusted not to the average patient but to specific ones.
- The construction industry benefits from predictive analytics at every stage, from budgeting to managing construction teams.
When it comes to the applicability of predictive analytics, education is no different from these sectors. So let’s see how you can use it to analyze and avoid risks, predict student behavior, and ultimately, improve the quality of your education.
Just like healthcare and finances, education is driven by numerical data. The chances are that you already have generous volumes of records to feed into analytical tools. Think grades, attendance, graduation rates, course and program enrollment, or even library records.
Finding patterns based on input data is the essential use of predictive analytics. By analyzing data sets from the last few months or years, predictive software solutions help you better understand how students interact with your organization. Which teaching formats bring the best student outcomes? What factors affect an exam’s passing rate? Applying modern predictive analytics methods will help you find answers to these and more fundamental questions.
Identifying Students at Risk
Student success can be attributed to a range of factors. For example, time and effort spent on studying, individual abilities, or resource availability. However, social considerations also impact students’ grades — and, consequently, their outlooks for the future. Teachers alone cannot be expected to take note of all their students’ issues, especially when dealing with large student groups.
Predictive analytics can help determine what makes some students consistently score As and others perform poorly. For instance, suppose a clear pattern shows that those who study an hour daily for a particular course pass the exam effortlessly. This is a clear signal for teachers how to help underperforming students improve their final scores.
Minimizing Drop-Out Rates
Student retention is one of the main concerns of schools and universities — and for a good reason. One study reports that 40% of undergraduates drop out of college, with 30% of first-year students not even making it to their sophomore year.
Data — when properly interpreted — can set a student who is likely to drop out back on a course to graduation. Thus, predictive analytics indirectly improves student grades but also helps them make the right choices.
If a student finds out that he or she is unlikely to qualify for the desired program based on historical data, a teacher can advise him or her on an alternative. With that knowledge, students who would otherwise drop out have better chances of graduating.
Personalizing the Curriculum
Knowing how metrics like attendance or student engagement pan out over time, you can adjust the curriculum to individual student preferences and levels. In addition, thanks to predictive analytics, you can identify unpopular courses and decide whether to modify or replace them.
Making the educational content more relevant and suited to the needs of your students can make all the difference between graduating and dropping out. This is especially important for freshers, who are the most likely to abandon their studies. If you learn that new students consistently fall short of the first-year requirements, you can act in time to prevent it, e.g. by updating recruitment procedures or adapting the curriculum.
Things Worth Remembering
Here are a few tips that will help you make the best use of predictive analytics tools in your school or university:
- Don’t trust the algorithm blindly. Remember that in the end, you’re in charge. So use your best judgment to decide if a data-based decision makes sense in a given scenario.
- Ensure that data governance rules are in place. For example, not all faculty members need access to all student information. To remain compliant, restrict data availability so that individual workers can only get the information they need.
- Keep students in control of their data. You’ll need their consent to use personal information, so be transparent about it. Demonstrate how sharing that information with you is in the student’s best interest.
Andrey Koptelov is an innovation analyst at Itransition, a custom software development company headquartered in Denver. With a profound experience in IT, he writes about new disruptive technologies and innovations in artificial intelligence and machine learning.
Featured Image: Max Hopman, Unsplash.