Transforming Raw Data into Student Behaviors.
Your retention initiatives need to be supported by a robust data strategy that includes all of your potential data sources and behavioral metrics that can be derived from them.
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.
WiFi Internet Usage
Universities spend significant time and energy optimizing their WiFi network and analyzing its usage across the campus, but they don’t consider how this data could affect student success.
As it turns out the network is an extremely robust way to measure student engagement without manual inputs or students downloading applications. Almost every student connects to the network, so this is a great data source to understand more holistically who is connected with campus and who isn’t.
Learning Management Systems
The primary repository for learning data and the primary focus of most universities analyzing student success, the LMS can be a powerful data source for understanding student behaviors.
Anything from clicks, logins, page navigation, session length, engagement, and assessments can provide useful information informing faculty and administrators the engagement level of a particular student.
If there is one achilles heel for the LMS, it’s that it is often deployed inconsistently, with different classes and majors utilizing the platform to different degrees.
Having a connected campus is an important goal for many universities, and this mission often involves building a robust student app.
Student apps can serve many functions, from providing campus life information (like schedules, maps, tours, parking, transit, etc.) to connecting individual students with messaging.
Having a university mobile app can enhance the student experience and provide strong engagement metrics for the students using it. As a data source, its one short-coming is that most university apps can’t maintain more than 50% adoption through the course of the semester.
As the passports that get you into buildings and onto the bus, a student is seldom with his student ID.
As a data source, the student ID card can provide some glimpses of student behaviors that would otherwise be unavailable, particularly around student spending habits and check-ins at secure buildings. However, without a significant university focus on collecting engagement data and emphasizing swipes, it often needs to be supplemented by more robust sources of information (such as app or WiFi).
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