Predictive modeling is the process of using statistical techniques to extract patterns from historical data in order to predict future outcomes. From finance to healthcare, and higher education, in particular, the use of predictive analytics is gaining currency. Embarking on a predictive analytics journey not only helps you attain better results and deliver great customer experiences but also has remarkable financial benefits. Let’s know how …
Maximize Student Success with Predictive Analytics
Emerging disruptive technologies like AI, predictive analytics have made it possible to have data-driven insights into customer behavior and predict what will or won’t resonate with them. In the context of education, deploying AI and learning analytics lets administrators see important student behavior patterns in an otherwise raw mass of data. It becomes possible by setting years’ old historical student data against a machine learning algorithm which can extract patterns from it to predict the maximum chances of student success. With every student record that this algorithm looks at, its ability to predict gets better. It builds predictive models to understand drivers of attrition and identify actionable triggers to help institutes reduce churn.
Tap into The Immense Potential Your Student Data Holds
Machine learning holds immense potential in the field of higher education if deployed, monitored, and, measured with a certain discipline. The constant focus today is on student-centric support, with one-stop student services, machine learning technologies; institutions today want to deliver a student experience that will take away the edge from their complex academic journey. Predictive analytics has come to play a great role in helping educational institutions achieve these student support goals.
Linda Hartford, CIO at Northeast Wisconsin Technical College in an article in CIOReview says “Higher education strives to achieve Amazon’s mastery of customer data gathering and use of Predictive Analytics. She, however, claims that the goal isn’t the same as Amazon when she says, “Higher education is not looking to determine how many widgets it can sell in certain markets, but rather, will a student who enters college persist, and wills/he completes a degree”.
Institutions today have a wealth of information about students right from when they are only prospects to their graduation stage. This includes high school transcripts, financial data, demographic data, progress and success data, course and program data, as well as data on extracurricular activities. Predictive analytics sorts through this mass of raw data to:
- Personalize student learning
- Enhance customer/student services
- Monitor student engagement levels
- Notify faculty and advisors about at-risk students
- Increase positive outcomes including higher enrollment and graduation rates
Propel Your Students Towards Success
The consolidation of academic, demographic and social data (obtained from social media, blogs, and other such channels) along with deployment of predictive analytics can put your faltering students back on track. It can give the instructors, administrators important insights into where a student is heading, whether s/he needs assistance and what can maximize the chances of graduation. While such unrestricted access to personal student information can raise some privacy and security concerns, the onus is on the educational institutions to ensure ethical use of this data while leveraging its benefits.
There are many success stories proving how Higher Ed institutions have benefited by leveraging predictive analytics modeling in their systems. Student retention has clearly been a motivational factor for universities to adopt predictive analytics. Not just that, statistics show that the application of predictive analytics has resulted in better learning and teaching environments. Is your institution using predictive analytics yet?