Student support when they need it, the proactive approach.
Transform unused network data into student behaviors you can act on
Common retention and student success tools are limited by the data they leverage. Grades, demographics, and historical data do little to identify how a student can be supported once they show up on campus. Understanding student behaviors is the critical missing piece needed for actually supporting students.
Unfortunately, while most student success tools enable users to manually input behavioral notes or flag students that need support, most students that are struggling fall through the cracks. Degree Analytics supports these students by automatically capturing key metrics like class attendance, so advisors can identify which students are not engaging and need positive nudges or advisor support.
How it works:
Students don’t go anywhere without some kind of smart device – a cell phone, laptop, wearable, etc. These devices are constantly sending signals that are captured by your network and stored. Unfortunately, this connection data is rarely utilized outside of one-off cases
Degree Analytics uses the massive wireless dataset at your university and applies our machine learning to automatically calculate hundreds of student behaviors – like class attendance, how students use campus facilities, or whether they’re taking advantage of campus resources.
With all this behavioral data at your fingertips, advisors and administrators can receive daily alerts and information from real student behaviors, so they’ll know exactly how to support students. This information can then be integrated with existing platforms (like CRM, LMS, etc.) to compliment existing tools.
Visibility into how students actually engage with your campus.
Every department of the University is responsible in part to the success and experience of students. Degree Analytics provides visibility into how students actually interact with the campus and resources so you know exactly what’s working, or how you can adapt to support their success.
Student Behavioral Metrics
Analyze more student behaviors that better align with student success
- Class Attendance
- Absence Alerts
- Context / Importance
- Student participation
- Online assessments
- Learning patterns
- Library Time
- Academic Time
- Class Engagement
- Community Spaces
- Campus Dining
- Engagement scores
- Event Attendance
- Campus Clubs
- Identify Trends
- Building Utilization
- Under-used Facilities
- Facility Management
- Total on-campus time
- Nights / weekends
Other Data Sources
Use new data sources that can drastically improve ability to analyze student success.
WIFI is the most universal data source leveraged across the campus, making it the best way to measure student behaviors.
LMS data provides isnight into how students utilize online learning materials, making it the most important data source for online students.
Campus apps are excellent resources for measuring student program participation and increasing student engagement.
Card swipes can complement network data in showing campus usage, but tend to be less robust and more manual.
SIS information is the foundation for understanding the background of a student and their current academic standing.
Incredibly flexible systems that allow advisors to track engagements with students, these systems can also be used as the “one-source of truth” for advisors.
Surveys provide strong insights and context, potentially answering “why” students take actions or complementing behavioral data with more “non-cognitive” information.
Social listening tools can help the university identify student sentiments and enhance student life activities.
Have +90% of class attendance automated in 30 days
Degree Analytics is the simplest student success platform you’ll ever deploy. There is no hardware to install or software to manually update. Faculty don’t need to change their routine, and the university does not have to change its culture. In weeks, not months, your team will have the data it always needed to support students before they are failing classes.