Transforming Raw Data into Student Behaviors.
Most universities stop at the SIS and LMS. While those are two powerful data sources, they are barely scratching the surface of what can be measured from a behavioral perspective.
University Data Sources
Use new data sources that can drastically improve ability to analyze student success.
CRM / Case Management
Some of the biggest innovations in retention technology are taking place with CRMs and Case Management systems (generally called student success platforms in higher ed). These systems have developed a huge range of features and use cases, and they can generally accept a huge variety of data to generate insights on the student.
From an advisory perspective, a CRM or case management tool is the best way to prioritize and scale advisor actions, and it can make a signficant impact on retention. If there is one opportunity to enhance the CRM or Case Management tool, it is by reducing its dependence on manual data with more standardized and robust data feeds.
Student Information Systems
The university has all kinds of information on the identity and progress of its students, and for the most part, it’s all going to end up in the SIS, the primary repository for student profile information and academic progression.
Almost any type of student analysis will involve data that is sourced from the SIS, and it serves several key functions for the university. However, a robust student analysis should not end with the SIS, because it has two limitations:
1) Most SIS is not behavioral data – so insights will be inherently less actionable.
2) Grades, one of the most important pieces of information in the SIS, are usually not dependable for predicting persistence of measuring engagement.
Because of these two limitations, it usuaslly makes sense to combine SIS information with other data sources (more behaviorally focused) to build a complete picture of the student.
There will always be elements of the student experience you cannot (or would not) quantify, such as intrinsic student motivations or how students work in groups, that are important to the student experience. To understand these elements and improve the student experience, surveys are often the best way to acquire this data.
Surveys deployed during orientation can provide excellent information that helps place students into risk categories, and surveys deployed during exit interviews can help the university understand why students leave. To compliment this qualitative data, universities can use their other behavioral data sources (like CRM, LMS, WiFi, etc.) to create a complete view of the student.
There is hardly a person, let alone student, left with access to the internet and no social media presence. Between Facebook, Instagram, LinkedIn, Twitter and others, there is a huge wealth of social data available on people.
When earned, through social-logins, this data can help identify the preferences and interests of students to help improve the student experience. Using more advanced techniques, artificial intelligence can also be used to help interpret social posts and generate friend networks. While there isn’t a lot of true behavioral information available on social network, it can be helpful information to enrich student profiles.
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 Groups
- Identify Trends
- Building Utilization
- Under-used Facilities
- Facility Management
- Total on-campus time
- Nights / weekends