In this issue:
- Where Current Retention Efforts Fall Short
- Starting with Fit: Defining and Delivering the Unique Student Experience
- Designing the Student Experience: Building Bridges across Student and Academic Affairs
- Delivering on the Promise: Removing Barriers to Student Success
- Identifying and Intervening with At-Risk Students
Even with a clearly defined student experience; close alignment of people, practice, and policy; and a concentrated effort to remove barriers to a student’s momentum in pursuing educational goals, some students will remain unlikely to persist. With a well-coordinated early warning system, an institution can intervene and provide or refer the necessary support to ensure more students stay enrolled and ultimately graduate.
Identifying At-Risk Students: What Data Are You Looking At?
The earlier an academically at-risk student is identified, the better the prognosis for their success in college. Early alert systems, implemented within the first four to eight weeks of a term, can be instrumental in beginning an intervention that can help facilitate students’ success and increase retention.
However, faced with frequent studies offering multitudinous data on factors influencing student attrition, it can be challenging to sort through the information available to determine what indicators deserve most attention, both to proactively identify students who may be at risk at a point prior to enrollment, and to drive early alert systems in the first weeks of a semester. To learn more, we interviewed Jennifer Jones, a clinical assistant professor and recently director of academic retention at the University of Alabama. Jones has developed a comprehensive and strategic approach to identifying and intervening with at-risk students, and offers her advice for:
- Making good use of your institution’s own historical data to predict risk factors
- Clarifying what real-time data matters
- Taking more than a one-shot approach to intervention
Predictive Historical Data
The past decade has yielded abundant studies citing factors that can contribute to the likelihood of student attrition. Cohorts often deemed at risk in the published research include the academically under-prepared, students who have taken a gap year between high school and college, students who work full-time, students who are enrolled only part-time, financially independent students who must bear the full cost of their education, students with family obligations, minority populations, first-generation students, etc. Before the semester even begins, it can be good to prepare for tracking students who occupy more than one of these cohorts. Many adult, nontraditional learners, for instance, will have a multitude of these contributing “at-risk” factors — work, family, and financial obligations may all be competing priorities that exert pressure on their ability to complete a degree.
However, Jones cautions that just looking to national trends is not enough. “Don’t take the national data as gospel. Too often, we forget to look at our own data,” Jones warns. The national trends may offer pointers as to what indicators to check for — but then you still have to check for them. To key in on the most critical indicators of student attrition for your institution, Jones recommends relying more on your institution’s historical data to help you identify the specific cohorts at your institution that may be at risk. Be wary of your own (and others’) assumptions, and review four to eight years of institutional data on the persistence rates of student cohorts that you expect might perform low in terms of persistence — and also of student cohorts you expect might perform high. Because of demographics unique to your institution, you may find surprises.
SCENARIO: HONORS STUDENTS WHO DON’T PERSIST
It can be tempting to assume that honors students will have a high persistence rate — and accordingly, to take them for granted. But suppose an enrollment manager at a large state institution examines six years of historical data for the institution’s honors students, and finds that they are actually persisting at a much lower rate than expected.
Why would students who are academically advanced be less likely to persist?
Further analysis reveals that a large percentage of the honors students were recruited from out of state. Despite an exemplary academic record, these students lived at a further degree of separation from family and from their support networks. There are other factors at play, as well. Despite an excellent high school GPA (which looks good on paper), some of the students lack study skills, and have been finding the transition from high school to college more difficult than they expected — while still other honors students have been finding their first-year college classes insufficiently challenging.
The lesson to be drawn, Jones suggests, is that you need to identify your own institution’s at-risk cohorts. Use the national data to help direct you as to where to start looking, but challenge your assumptions and place the greatest reliance on your own historical data.
Beyond examining particular student cohorts, Jones recommends looking at historical data for particular courses. A few institutions have seen great strides in their ability to predict and identify at-risk students by means of DFW assessment. In this case, the point is to identify which first-year courses show the highest drop, fail, and withdrawal rates. Your historical data can empower you to predict which first-year students are likely to face the most difficulty — based on which courses they are registering for.
For example, if you know in advance that your math courses have a high DFW rate, you can move proactively to support the students enrolled in them with supplemental instruction, tutoring, and other interventions. You can encourage faculty to integrate supplemental instruction or math labs into the syllabus and the course. “You can offer the students opportunities to achieve greater success,” Jones remarks, “before they even have the chance to fail.”
Real-Time Data: What’s Most Critical
Finally, you can generate alerts that trigger particular interventions for at-risk students based on data provided in real time during the term. Examples can include:
- Invite faculty to alert you to warning signs within the early weeks (e.g., missed attendances, signs of depression, etc.)
- Identify the weeks in the term that have the highest withdrawal rates, and reach out to students who withdraw
- Design alerts based on midterm grade reports (does a student have one C-? Two? Three? You can set up tiers of different priorities of alerts)
Planning for Intervention
Once you know you can identify the students who are most at risk academically at your institution, the question is how to reach out to them and refer them to someone who can help them. Jones suggests:
- “Residence halls are your best shot if you are a residential campus,” Jones remarks; based on midterm grade reports, identify the dorms where you can make the greatest difference, and coordinate with the residential staff to plan for outreach to at-risk students.
- At a commuter campus, determine the best way to reach a particular at-risk demographic, and send regular updates (whether by email, a Facebook page, or alerts to a mobile phone)
- For a large campus, establish a student call center
THE CALL CENTER
At the University of South Carolina’s call center, freshmen get called twice by peer mentors during the year. “Supply cell phones with 1,000 free minutes,” Jones suggests, “and have your peer mentors call to find out how at-risk students are doing. Students are more likely to respond to other students in a casual conversation.”
Other interventions that can make a difference include:
- Outreach to students who are on academic warning after their first term
- Asking the registrar for names of students who have not registered by the last day of registration, and reaching out to them — “this process may help students commit to returning, and may also identify what students are at risk of not returning”
Most of all, Jones suggests, it’s important to be strategic in your timing and understand the points on your academic calendar when students are most in need of support. “It’s not about just looking at your data,” Jones remarks, “or having one good early alert system in place. You need to look at the entire cycle from the student’s perspective.”
“It’s not enough to introduce our support services and other resources to students at orientation or in the syllabus; it’s not relevant to them then. They don’t know they need these resources, so they disregard them. Watch your own academic calendar. At what points do you see more withdrawals? When do your students realize they are in trouble? That’s when you need to put out alerts through email and through the faculty in your classes. Put the messages out when those messages are most relevant for the students.”
Jennifer Jones, U of Alabama
Develop a Comprehensive Retention Plan
In this issue of Higher Ed Impact: Monthly Diagnostic, we have drawn from some of higher education’s leading experts on student success to offer insight into what a comprehensive approach to retention looks like — from defining the educational experience students will succeed in, to letting that definition drive recruitment strategy, to aligning people and policies with that promised student experience, to identifying and intervening with those students who are most at risk.
Want Tactics for Specific Populations? Read These Articles in Higher Ed Impact
Key Strategies for Retaining Men
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Improving the Academic Success of Latino Students
Helping Chinese Students Transition for Academic Success
Improving Community College Student Success