Nov. 16, 2021
Imagine a student struggling to keep up with the material in class and receiving a series of continuous email/text message alerts to see a professor or an academic advisor because she/he is on the verge of failing, or worse, academic probation. Patrick is that student and shared with us just how off-putting these alerts can be. Patrick is a first-generation college student at a 4-year public institution, enrolled in a microeconomics course this fall semester, yet is experiencing difficulty understanding the material. To remedy the issue, Patrick talked with his professor after class seeking additional help, however, the professor did not communicate any reason for Patrick to worry. Yet, a few days later, Patrick received ongoing email/text message alerts that he is failing his microeconomics course and he should schedule a meeting with his academic advisor. Patrick felt betrayed and confused.
This is what happens when there is a misalignment between the intention and implementation of an early alert system, and the resulting student experience at the receiving end. This is why student voices must be included in the planning and implementation process of adopting an early alert system. If students’ voices are not included, colleges make the mistake of relying on technology as their silver bullet to meet institutional goals of retention and completion — while ignoring the student experiences behind those data points and missing the mark.
Early alert systems are communication advocacy tools used for identifying academically at-risk students, improving student retention and providing support for college communities. They serve institutions with two components: alerts and interventions. Alerts are designed to be a proactive feedback system that flags at-risk students. Interventions are the next steps to alerts and they include strategic outreaches to address the problem and the student’s potential need. These two components help colleges gather demographic and performance data to predict student behavior and intervene when necessary. Using data in this way is known as predictive analytics. While these systems are intended to provide wrap-around services to students, early alert systems sometimes fall short of their promises in actually meeting college students' needs.
An example of the use of alerts and interventions is a student who is working full-time, gets a flat tire on her way to a class she is academically performing well. However, because she does not have enough savings to pay for a new tire immediately, she misses consecutive weeks of class until she can afford a new tire to drive to campus. In an ideal world, this student receives alerts with culturally sensitive language to schedule time to chat with an advisor to understand her abrupt absence. It is during this meeting that the advisor discovers it is not an academic intervention that is necessary, rather a financial need and immediately works with the financial aid department to release emergency aid to the student. Its the timeliness of the alerts, coupled with adequate intervention aligned with the students’ needs that optimizes the use of early alert systems.
Early alert systems became very popular over a decade ago, primarily among four-year private and public institutions. Research suggests that roughly 93% of institutions use them. More recently, community colleges have also become active players. With community colleges enrolling primarily underserved students (e.g. Black, Latino, low-income and working adults with families), the impact of alerts can carry different implications for them compared to their white and traditional college-aged counterparts.
It is important that students' voices are included in the planning and procurement process of vetting vendors because students provide the perspective that faculty, advisors and technology experts simply do not have. A strong procurement process involves stakeholders of diverse backgrounds and experiences, where student voices are included as well. Students can share feedback on what different kinds of alerts mean to them to inform the type of language to use and whether systems are user-friendly for students.
Little has been written within the literature on how predictive analytics within early alert systems fit into the community college sector. Our upcoming work on this topic will address this gap to include research on the extent to which community colleges are using early alert systems. We will use this work to better understand whether these systems effectively improve retention and completion outcomes for the students they serve, particularly for Black and Brown students, and develop actionable recommendations for community college leaders.
Stay tuned to continue to follow our work on this project!
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