People are continuously questioning the purpose of obtaining a college degree, as well as its benefits. This recurring problem is not about anyone opposing student success; it is about perspective. We must align our definitions of success, in its varied manifestations.
For example, a student who enrolls in a university to study engineering discovers that they dislike the subject and transfers to another school. Is this outcome a success even though it negatively impacts the original school’s retention rate?
From an institution-centric perspective, it appears that the institution failed that student. Schools are held accountable based upon aggregated rates of student success, such as their retention and job placement rates. For individual students though, these are all or nothing measures. They either graduate or not, get a job or not. A 97% job placement rate is very impressive for a school, but what about a student who comprises part of the remaining 3%? Students may feel like “just a number” if educators only concentrate on the strategic, aggregate definitions, without providing them individual attention or the chance to personalize their learning.
A learner centric approach serves both interests. Retention rates are changed one student at a time. Every time our efforts make a difference for one student, it positively impacts the broader retention demands. Big data and analytics can be used to ensure that students aren’t treated as numbers. We can scale personalization and learner-centricity by democratizing data through early alert systems and labor market data.
Democratizing Data
Democratized data empowers an individual to take action. There needs to be certain safeguards to ensure members of your institutions have access to appropriate data, at the right time, so they can make the best decisions for their students.
The chief difference between the student success data a VP of enrollment would consider compared to an advisor, is in the level of aggregation. The VP level will likely leverage these in aggregate to make the broadest strategic impact. The advisor can take more tactical action, prioritizing outreach and intervention. Even learners themselves can leverage analytics to make data-informed decisions that embrace their autonomy and self-actualization while also strengthening the unique bond between them and the institution.
Early Alert Systems
Early warning systems are another burgeoning trend in student success analytics. These are frequently based on myriad data sets. Attendance data, for example, can be aggregated and reported to external stakeholders such as Return to Title IV funds (R2T4) to establish clear strategic guidelines for engagement policies. Advisors can use this information to prioritize outreach and intervention, often detecting student risk before it is obvious.
Sharing with learners themselves, provides a new level of agency & control, “simply making students aware that they are at risk…motivates them to seek help and change their academic behavior”[i]. Once again, a simple set of data, shared across myriad stakeholders whom all embrace learner-centricity, amplifies the efforts of each stakeholder.
Labor Market Data
Each degree bears a multitude of learning outcomes, but students often miss the ability to articulate those outcomes into tangible skills. Certain degrees are more straightforward than others. A student who wants to be a nurse will almost certainly pursue a nursing degree. But what about students who do not achieve upper-division status, do not meet practicum requirements, or choose to change careers? It is difficult to position those specific skills in the context of a broader labor market.
As the labor market changes, career services teams can use these labor market data to assist their learners in creating dynamic and effective resumes that are responsive to an ever-changing labor market. Furthermore, having these data in the hands of a learner empowers action. Students can select a course that is not only interesting but also fills a skill gap. Showcasing and articulating these developed skills epitomizes a learner-centric approach that is fueled by data.
Conclusion
To ensure that our students achieve self-actualization through meaningful learning experiences, we must develop a shared understanding of what success looks like for them. We then must share these crucial data insights with learners so they can make wise decisions and champion their success[ii].
Higher education leaders can be proactive in this pursuit by promoting innovation through a learner-centric, data-informed strategy. Doing so will demonstrate the superior value of a postsecondary education and will prepare your students for success now and in the future.
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[i] Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6-47.
[ii] Almond-Dannenbring, T., Easter, M., Feng, L., Guarcello, M., Ham, M., Machajewski, S., Maness, H., Miller, A.P., Mooney, S., Moore, A., Kendall, E. (2022). A Framework for Student Success Analytics. EDUCAUSE. Retrieved from: https://library.educause.edu/resources/2022/5/a-framework-for-student-success-analytics.
