How would you like to get more from your data collection efforts without doing more data collection?

I’m talking about getting more out of your surveys, rubrics, engagement data, and even your event attendance tracking information without asking more questions or taking up more of students’ time. And how would you like to do it without having to maintain or rely on your own records of data and information about students?

Let’s talk about data integration. Most institutions have multiple data sets stored in various places and within various systems: Student enrollment and records information (including demographics, address, transcripts, financial aid, and more) is stored within your Student Information System (SIS), class participation and grade data is stored within your Learning Management System (LMS), student participation or engagement data is stored within a student engagement platform, and your assessment data may be stored in a dedicated Assessment Management System (AMS).

Through a little technological magic, we can link and connect these data sets. And by “we”, I mean that you’re likely going to need to bring in your fellow campus data guardians. While some collaboration may be needed, connecting these data sets is possible and beneficial for multiple parties.

I’ll talk more about the how later on; first let’s talk more about why you would want to do this.

TOP BENEFITS OF CONNECTING

Let’s focus just on student affairs professionals connecting to their Student Information System (SIS). If you are able to tap into this, you could shorten your data collection efforts on forms and surveys and eliminate the need for students to self-report information about themselves, such as their majors, identity demographics, and class years.

All you would need is to collect a common identifier (such as institution email or ID number), which will enable you to pull in that student’s information from the SIS.

Beyond reducing the number of questions you’ll repeatedly ask students to answer, integrated data systems can help ensure more accurate and consistently collected data.

Consider these scenarios:

• The Office of Student Activities needs to know the class year of students participating in events in order to better gauge the campus’s programming needs. However, different professionals within that office have been collecting that data in different ways for the programs they’re in charge of. One person collects class years via first-year through senior options, while their coworker surveys students on their credit ranges, and a third coworker asks if attendees are undergraduate or graduate students. The office is left to reconcile these various response options, which may or may not coincide with institutional classifications.
• For Women’s History Month, Career Services wants to know the identities of students attending events in order to gauge the effectiveness of the office’s marketing campaigns. In one instance, they ask attendees to respond to question asking about sex, and, in another, they ask about gender. As those are different data points, they can’t be reconciled. Moreover, if the institution only has one of the two on file for students, there is no accurate way for Career Services to know what percent of the total given student population they are attracting.

I’m sure you can identify with these data dilemmas and pain points. The confusion around institutional definitions or classifications, as well as accuracy and consistency of student information, can all be taken care of by tapping into the SIS instead of asking for students to repeatedly self-report.

Plus, as I alluded to in the career services example, when we have access to institutional data sets and utilize them, we gain insight into equity considerations. We can accurately know our student populations, making it easier for us to disaggregate (separate complete data set into distinct groups of data) our collected data related to engagement, satisfaction, and performance.

With disaggregated data, we can see where the identities of students significantly differ from our goals or learning targets. When disaggregating data or looking for differences related to identity, it is important to either compare identity populations against an institutional target or set population-specific targets. Be careful not to compare identities against one another, as that can make it seem like you view one population as superior over another.

All of those examples are just pointing to connections within the SIS, but if you add in the LMS, AMS, and other data systems, you’ll have a recipe for some awesome collaborations. Imagine being able to talk about student population behaviors or trends that you can link to classroom engagement/performance, identities and demographics, major/credits earned, and more. You could invite student engagement and advising staff, the registrar, and faculty to consider things like course load, completion, and progress of involved students.

Such rich and robust data connections might assist you in making progress toward understanding big, complex concepts — like the connections between a student’s sense of belonging and academic success.

(Live link here) Continue to read my four steps toward data integration and personal experience at National Louis University.