Improving student success starts with the right data.
Exclusively depending on faculty inputs and manual data entry means students will fall through the cracks. Automating alerts means no student is left behind.
Waiting until midterm grades or “Alert Week” means students are falling further behind. Support students when they need it with daily risk evaluations.
Behavioral Risk Factors
All-star students should not be “at-risk” because of their profile. Rewrite the book on who needs to be supported based on student actions, not their background.
Changing the Student Success Dialogue
For years, the focus of the student success conversation has been around the student groups that are “at-risk”. Before they even arrive on campus, dozens of student groups receive labels and prescriptions based on factors that are completely out of their control. First-generation students, financial aid students and students with lower test scores are often among the students we focus on.
But are all of these students really “at-risk”? Of course not. In fact many of these students will be the future leaders of their class.
We believe a student’s actions should speak louder than their background and personalized support needs to be more focused than grouping large portions of the incoming class into demographic buckets.
To do so, our team focuses on student behaviors as the primary factors in prescriptive student advising and programming. Behaviors like class attendance and co-curricular engagement are the keys to providing personalized support to every student, and making sure that no student is left behind.
Measure attendance for every class and every student, with more accuracy than manual methods, using the wireless data your university is already capturing.
Identify the top risk factors for student groups, which programs have the biggest impact on student success, how the campus is utilized, and more using de-identified data.
Proactively identify students that need support, analyze campus engagement and programs, and integrate with any CRM or case management system.
How accurate is your class attendance measurements? What about false negatives?
Our class attendance metrics are on average between 90 and 95% accurate, which is typically more accurate than a professor taking attendance in a class of more than 25 students.
How do I manage privacy concerns?
Protecting the privacy of university students has been our team’s number one focus since our company’s inception. We have built all of our tools to deliver “privacy be design”, and we provide guidance on how to provide transparency with your students. For more information, please visit our Privacy Page.
Do I need to train my team on another platform? What systems do you integrate with?
While we offer a platform that allows users to realize the full value of the network, we also integrate with any CRM, Case Management System, LMS, or Student Success Platform. If your team is suffering from “technology fatigue”, there is no need to introduce another platform.
I have successful student success initiatives and advising programs, will I be starting from scratch with Degree Analytics?
No, our solutions are designed to be flexible and fit into a university’s existing student success structure. We scale processes that your team already executes through automation, and we provide data and analyses on programs that your team already manages.