As enrollment in online programs continues to grow, administrators across higher ed are faced with the challenge of providing targeted support to help students succeed. Many opportunities to address this challenge exist within the vast amount of student data that is collected through enrollment and participation in online courses.
That is why the most forward-thinking leaders in online education are leveraging the data they have to build predictive analytic models and develop holistic student success strategies based on that data.
In this 10 minute podcast, Ellen Wagner, Chief Strategy Officer of the Predictive Analytics Reporting Framework, illustrates key points in the implementation of predictive analytic structures.
In review, Wagner recommends taking a close look at these three questions before implementing a predictive framework:
What will predictive modeling give you that your current strategies cannot address?
The progression of predictive analytics has opened new avenues for data driven student support that can make an impact beyond current best practices. However, without a clear direction and purpose behind these analytical ventures, predictive modeling can also be a risky endeavor that requires significant amounts of time and resources.
By asking the question first of how analytics will be used, institutions can more effectively align the goals of the predictive analytic framework with the pressing challenges the institution has struggled to address.
What research questions will lead to the most actionable results?
Tailoring your research questions to guide the development of your predictive model is essential to seeing the impact of predictive analytics. Institutions must align their research and data collection with an actionable outcome that will make an impact for the student.
“Simply knowing who might be at risk really isn’t enough. If you don’t know what to do to mitigate that risk or to respond to the needs of that student you are really only halfway there.”
Ellen Wagner, Predictive Analytics Reporting Framework
In designing the analytics framework, it is key to think beyond the tracking of student behavior and risk factors, and continue the research into the effectiveness of the solutions your institution is implementing.
What should institutions be doing to prepare for a shifting mindset?
For many in higher education, the idea of reporting through the use of analytics and data has always served the purpose of evaluation and assessment. Implementing a predictive framework that looks forward, not back can be a substantial shift in thinking.
Making sure to convey this shift to stakeholders across campus will help garner more buy-in and support, making the implementation and completion of the project a smoother process.