Page 6 - CT Innovation in Education, June 2022

INNOVATION IN EDUCATION | CLOUDERA – LEARN MORE AT CLOUDERA.COM
Using Data Analytics
to Enhance Student
Services
Improving the student experience and student performance hinges on modern approaches to capturing and analyzing data.
THE ABILITY TO AGGREGATE AND
analyze data is central to institutions’
capacity to identify trends and find ways to better meet the needs of students. In fact, I would argue that those improvements are not possible without data analytics.
If an institution evaluates withdrawal rates across student populations, for example,
it can reveal valuable insights about a particular subject or even a single class offering. Unfortunately, data capture is often rudimentary on campuses. Individual systems have different methods of sending data files, and manual intervention is often required to move files to a central location, with some scripting involved to string together the datasets. Those processes are difficult and time-consuming to develop, maintain and troubleshoot.
There are more efficient and secure ways
of capturing data. For starters, institutions should take advantage of automation. When they move away from manual processing
and shift to configuration-based data-flow technologies, they reduce the time it takes to develop pipelines for capturing data. In addition, many of those technologies provide a graphical user interface, which further streamlines and simplifies analysis.
BRIAN HAGAN
Senior Solutions Engineer Cloudera
6 | CAMPUSTECHNOLOGY.COM
Ensuring the Successful
Adoption of AI
In addition to automation, artificial intelligence can transform the way colleges and universities provide services to their students. When higher education leaders understand the value and availability of AI, they can create a vision for
its adoption. Then data analysts can use AI to accelerate the institution’s delivery of student services and improve its ability to predict outcomes early, enabling educators to address trouble spots early or invest in key initiatives.
With AI, analysts can focus at the level
of the entire student population, a certain demographic profile or the individual student. For example, AI can integrate with a campus learning system to identify students who may be at risk of dropping out.
To be successful, AI must be part of an institution’s overall data management strategy, and the IT infrastructure should be built or updated to support that strategy. Choosing
the right AI technology involves carefully considering how it will fit into a broader, long-term IT model and the existing security policies. That’s the best way to ensure that the institution’s data scientists are operating in a manner that adheres to established policies.