How higher ed can get the most out of advanced analytics
With more data comes more powerful algorithms that can play a role in everything from improved inclusivity to fewer instances of fraud
- Higher ed should leverage new data models as they become available
- With more data comes more powerful algorithms
- See related article: Universities struggle to capture data’s value
- For more news on advanced analytics, see eCN’s IT Leadership page
The data explosion of the past decade has ushered in another era of innovation, as evidenced by the rapid popularity of ChatGPT, a generative AI tool that recently took the world by storm. The potential for algorithms to impact and improve workflows has never been greater, yet many institutions are struggling to effectively use the data they have, much less reap the benefits of cutting-edge applications. It’s time for that to change. AI and advanced analytics can and must serve as trusted advisors for all parts of the higher education ecosystem.
Top applications for advanced analytics
The first area where algorithms can have a tremendous impact is regarding operations. Rising administrative costs are one reason college tuition continues to rise. By leveraging advanced analytics, institutions can streamline operations and improve efficiency. For example, an important but time-consuming administrative task is identifying fraudulent financial aid applications. Far too many institutions do so manually, which is not a good use of human resources. Flagging fraud is a task well-suited to AI, though, as machine learning models can easily keep pace with ever-changing fraud schemes. By handing tedious work off to AI, those who work in the financial aid office can spend more time supporting actual students.
Of course, supporting students in the classroom is equally, if not more, important. Advanced AI can help here as well, by suggesting individualized content and exercises that align with any given student’s preparation level and learning style. As chatbots and self-directed learning offerings continue to get more sophisticated–thanks to the sheer amount of data and computing power available–instruction supplemented by AI will become the norm. Institutions must embrace these technologies to remain competitive and foster a truly inclusive learning environment.
Finally, AI and advanced analytics can help institutions have a more granular understanding of student success. Institutions must know how to choose a focus area, select a desired outcome, and monitor whether a particular initiative is solving the problem it’s meant to. That might mean closely tracking retention, following student paths through the institution (including where and when students drop out), or even tracking changes in the labor market. This is the biggest area that higher education can improve, as institutions continue to struggle turning data into action.
Three tips for success
Despite the potential for advanced analytics to improve higher education, many institutions face paralysis in one way or another. Siloes represent a common hurdle to truly reaping the benefits of advanced analytics, as there’s a tendency to let departments pick their own systems. Disparate platforms can’t talk to each other and thus only offer a partial picture of institutional trends. The only way to lay a lasting foundation for AI and advanced analytics is to implement a common centralized data environment.
At the same time, many institutions struggle to secure employee buy-in for technological initiatives. Starting with small pilots and working to foster a data-curious culture are two great ways to get the ball rolling. Begin with a small group of committed people. Then, based on the results of the pilot, deploy the technology on a slightly larger scale. Throughout any deployment, nudge people to think with curiosity. This might mean providing training opportunities around data literacy and new analytical skills. It’s common for new technology to be met with fear or skepticism, but developing a culture that values analysis is the best way to overcome hesitancy.
Implementing a common data environment and data-curious culture now is crucial because algorithms will continue to evolve. Higher education organizations need to be ready to embrace new models and data sources. For example, some institutions are working to add K-12 and early childhood data to the mix. Having an entire life view can help institutions offer the right services and support, particularly for students who don’t appear to be as academically prepared.
The bottom line
With more data comes more powerful algorithms—algorithms that can play a role in everything from improved inclusivity to fewer instances of fraud. That’s why institutions need to lay the right foundation today. When it comes to technology, change is the only constant. To get the most out of advanced analytics, higher education needs to be nimble enough to leverage new models as they become available.