Using Data to Improve Student Retention

The “data-clock” has already started ticking. Through this series we have discussed that institutions must be:

• Standardizing their data infrastructure – Data Warehouse
• Organizing personnel to work cross-functionally – Train staff to leverage their own data
• Establishing strong effective data governance – Hold staff accountable for the data
• Sharing data broadly – Broad access and critical review across silos
• Working smarter – Spend less time on basic data reporting and more on analytics

Why We Care About Student Retention

Student Retention requires a paradigm shift in Higher Education. The old paradigm: “Look left, look right, by the end of the term…”

Being a good instructor and a strong institution is not about the number of students you fail, but the number of students who learn the information and are successful.

Many are stuck in the old-paradigm and will require reasons for improving retention:

• We care about our students
• Each % retained = significant increase in enrollment = significant $$
• Institution’s Reputation – students aren’t going to go to institutions where they have a very high probability of failure

What We Know About Retention

Every semester some students won’t return. There are very well documented groups that are at higher risk:

• First Generation
• Low-SES
• African-American Males

What Most Don’t Know About Retention

What is the cause behind your students exit?

Being First Generation, from Low-SES, AA-Male is not a cause. Finding some causes requires investigating non-returners.

Non-Returner Research

Non-returner research is important to conduct regularly. We cannot assume that the cause(s) will remain the same for different generations of students.

Some common reasons include:

• Money
• Life (work, health, family, etc.)
• Lack of academic progress
• Planned exit (transfer, obtained 60 credits, etc.)

The goal of Non-Returner Research is to identify the causes and identify groups impacted.

Retention Research

Non-returner research can provide the self-reported cause of why the students left.

Are some of these causes solvable? How many students with that cause are already planning to not return next semester?

Simply surveying the student body can provide insight into students who have already decided, mid-term, that they might not be returning next term.

The Crux of Retention

Knowing which students may not return is easy. Doing something about it can be much more difficult.

Number of advisors/staff

Resources available

May need to restructure offices/staff and target resources.

What’s Possible and Should Be

“Early Alerts” are based on student behavior (Cognitive Institution). Data is fed into AI to identify at-risk students who are then targeted for interventions.

Behaviors such as:

• LMS activity (including individual exam grades)
• Attending class
• Advising meeting activity
• Meal plan, gym usage, etc.

Solving Retention

Solving retention is possible. It requires collaboration between research and academics, advising, etc. It also may require reorganization.

The alternative – Your institution is perfectly designed to produce the results you’re getting. Without significant change in design, there will be no significant change in results.

Series Wrap Up

Institutions must be more intelligent and nimble. Improving your effectiveness requires accurate data that shows where the institution has been, where it is at now, and where it will be in 2-3 years. While the series has focused on technology recognize: It’s always 10% technology and 90% people.

To watch the entire recorded webinar series, click here!