Student Affairs’ Use of Engagement and Behavioral Data – 4 Insights from the Field

It is not a secret that many institutions are seeking ways to leverage data to better serve students. As retention strategies are now relying on more sophisticated analyses, particularly predictive analytics, some student affairs administrators may ask two popular questions – what do I need to know about these models and how can they help my daily work? This blog will share a few highlights from NASPA’s recent examination of how institutions are using behavioral and engagement data to inform student-focused practices and initiatives. The discussion will be framed around four things that student affairs professionals of all experience levels should know about how these data are being used.

1. Some of the most useful data analyses can be conducted without sophisticated models.

In 2014, The Education Trust released a report that offered 10 analyses that can provoke discussion and action on college completion. The authors of the report suggest that “the initial work for these analyses does not take great data prowess, but instead simple and compelling analyses that dramatize the problem and invite broad-based problem solving.” This report supports NASPA’s findings, which suggest that many institutions collect data that if analyzed correctly, could reveal important insights into students’ behavior. For example, one institution reported that it examines data from student identification cards to find students who are visiting the fitness center soon after it opens in the morning or shortly before it closes at night. The institution determined that some students whose identification card activity reveals consistent patterns of such visits may not be visiting the center to simply exercise, but perhaps also to use a shower because of a homelessness issue.

2. Senior-level commitment is critical to any successful data-informed initiative.

NASPA’s research revealed that one of several primary factors that appear to influence institutions’ use of predictive analytics is that senior-level leaders are encouraging a campus-wide culture of data-informed decision making. In fact, the majority of respondents reported that their institution’s commitment to leveraging data for student success is emphasized publicly by senior leadership, so much so that it is evident in their mission statement and strategic plan. In many instances, senior leaders are setting and managing expectations for data-informed decisions, including the sometimes tough task of establishing data governance policies. This is especially important as institutions will need continuous training and support for personnel who routinely collect, analyze, or utilize data at the program, unit, or institution level.

3. Institutions are using thoughtful interventions to help students who may need additional support.

Twenty-three of the 25 institutions that NASPA interviewed reported the use of an early alert system. These systems utilize several types of data to identify students who may benefit from an intervention within an academic term. Most institutions use early alerts as a retention strategy, as the goal of the practice is to offer help to students before they reach a point where they are close to leaving the institution. For example, one institution reported that when a student who is doing well academically requests a transcript, that triggers an action from an advisor. Before the student’s transcript request is granted by the registrar, an advisor will call the student to inquire about the kind of support that could persuade the student to not leave the institution.

4. Two important student-related concerns are data privacy and appropriate communication.

Although behavioral and engagement data offer timely insights into students’ connectedness with their institution, it is imperative that professionals maintain high levels of data integrity, especially with regard to the protection of students’ information. Many institutions reported having ongoing discussions about the appropriate level of information that students should have about how the data from their identification cards and use of mobile applications is used. For example, some institutions use location tracking to identify when students who are in need of additional tutoring are physically located near an area where such instruction is offered. In such situations, the student receives a text notification that services are available near them. While these timely interventions could prove very helpful to students who need them, there is a risk that such practices, if not executed thoughtfully, can result in students feeling as though the institution is infringing on their privacy. Institutions must also be careful when informing students about how and why they have been selected to receive additional support. Student interviews revealed that words such as “at-risk” and “tracking” could have negative connotations.

It is obvious that student affairs divisions not only gather data that are invaluable to strong predictive models, but professionals who routinely use these data are well-positioned to continue influencing the development of effective innovations to help students succeed. While the four aforementioned things offer a good foundation to describe the current landscape of institutions’ use of data, more work is needed to fully understand capacity differences by institutional type. Therefore, NASPA will continue its research on institutions’ use of engagement and behavioral data with a national survey. For example, several institutions reported that one challenge of using data from student identification cards, mobile applications, and other sources is that it is typically collected via several different methods and it is sometimes difficult to integrate with data that is stored in the student information system. The national survey will help determine whether these challenges and others are unique to certain types of institutions.

References

Yeado, J., Haycock, K., Johnstone, R., and Chaplot, P. (2014). Learning from High-Performing and Fast-Gaining Institutions. The Education Trust.

Burke, M., Parnell, A., Wesaw, A., and Kruger, K. (2017). Predictive Analysis of Student Data: A Focus on Engagement and Behavior. NASPA. 


Download the full Predictive Analysis of Student Data report from NASPA's Research and Policy Institute.