There is a real push among regulators and others concerned with the quality of education for schools to have more student data. But for the most part, the data these people are concerned with is what I call output data: graduation rates, job placements, earnings. We spend a lot of time thinking about these things, but they’re very blunt measures that don’t tell us much about how to actually improve learning or academic outcomes.
What I’m most excited about when it comes to student data is the ability to see fine-grained analyses on a case-by-case, student-by-student basis—and use them to markedly improve student achievement.
What Student Data Do Schools Need?
All schools can benefit from having a more complete, more holistic view of their individual students. You’ve probably heard this referred to as a 360 view of the student. But what, really, does having a 360 view mean? How do you know when you’ve achieved it? I will illustrate with an example:
If I’m serving an adult learner who is across the country and engaged with us in an online program, I want the most complete picture of that student and how they’re engaging with us—which will let me get ahead of issues before those issues get in the way of that student. I want to know as much about them as possible so that I can make sure I’m delivering just the right support at just the right time.
If my student has called the help desk four times in the last 36 hours, that’s a huge flag. For most schools, the help desk ticketing system sits in isolation. There would be no way for an advisor to know that this problem is brewing in order to intervene in a timely manner with support services.
However, you can set up your technology so those help desk calls flow into the advising CRM, and then you build that CRM so that when it sees, say, three calls in a certain time frame, it shoots up a flag to the advisor. That advisor can pick up the phone and say,
Advisor: Hey, Bob, I understand you’ve been making a lot of calls to the help desk. Anything going on?
Bob: Oh, geez, my computer isn’t working. I have an assignment due tomorrow that I’m trying to get done…
Advisor: Okay, let’s work on a strategy. How about we ask for an extension? I can send you a Chromebook to help you finish your assignment.
This is a great example of getting access to and effectively using a 360 view of the student. When you can knit student data together (including everything from financial aid information to community engagement to calls to the help desk and more), you can get a full view of what the individual is up to and up against—and it’s incredibly powerful. You can actually intervene with the correct support right when it’s needed the most to keep the student on track.
As another example, we can use our student data to do predictive analytics that can tell us within one or two percentage points how well a student is going to perform in their first semester. If the student is at higher risk based on our analytics, we can then wrap more support services around them from the beginning, provide more proactive advising, and we can give them a higher watch profile that we’ll look at more closely throughout the semester.
Advancing the 360 View with Advanced Analytics
We’ve been able to achieve useful 360 views of our students at both Southern New Hampshire University and College for America (CFA) (the difference being that it’s harder at SNHU, where we don’t have Salesforce to integrate our systems yet!). However, I would say that the 360 view is never complete. There are always additional things we can be learning about our students and how they are engaging with our institutions, all with an eye for better serving our students.
I’m excited to put our data and analytics to use in the near future toward ends like:
Understanding the patterns of students in competency-based education
When you’re in the credit hour world, you have rigid term structures; it’s easy to see what each student accomplished in each term or semester. In a non-credit hour framework like CBE, students set the pace. Sometimes we look at a student in the system and it appears that they’re doing very little, but then they will have a big spike of completing competencies. Then there are other students that have more constant activity. We’re seeing a lot of variation. We’re trying to understand what that tells us about the learning in a CBE model.
Determining the effectiveness and efficacy of learning content
I’m really interested into being able to get good insight into the efficacy of different learning resources. I’d like to answer questions like: If different students spend more time with the learning content and perform better, is that a causality or a correlation? Are there certain kinds of content that are more effective for certain kinds of learners?
Building cognitive learning paths
We want to do more with unstructured data to build cognitive learning paths where we can predict which types of content students will do best with and align them with appropriate assignments. Bob may do really well with one kind of learning, which matches with a particular cognitive map and certain types of assignments. Sally, in contrast, will be fine with some of those things that challenge Bob, but she’s really going to struggle when she comes up against X; let’s give her assignments that look like Y. When we can start to do this build cognitive paths like this, we will be able to build highly-tuned, individualized learning pathways.
What’s Getting in the Way?
When we have visitors come to SNHU and see how we use data, it’s often a jaw-dropping experience for them. On one visit, we were hosting a prestigious New England university. After giving them a demonstration of the kinds of data we look at and what we can ask of the system, the provost turned to one of his vice presidents and said, Why can’t I ask these questions? How come I don’t know this?
The 360 view of student is a fairly ubiquitous concept at this point, and most institutions would agree that they need to work towards it. So what’s getting in the way of schools accessing and using this data in a meaningful way?
Most institutions are up against a few common barriers. To start down the path of more meaningful data and analytics, you need to ask yourself:
1. Do I have a system that yields the student data I need?
Many institutions have a very silo’d technology infrastructure that reflects a silo’d university. There are many technologies in place, and they usually have varying degrees of integration and interoperability. This makes it challenging, to say the least, to pull data and pull it in ways that are meaningful. Having an agile platform, such as Salesforce, that serves as a system of engagement or intelligence and provides easy, real-time access to all parties (administration, educators, coaches, etc.) is an incredibly powerful foundation for schools to start from.
2. Do I know what questions to ask?
Data is a neutral thing. We need to ask the right questions of the data to actually get at the phenomena we’re studying, whether it’s student persistence, or outcomes or anything else.
3. Do I know what to do with the data I get?
Data for data’s sake obviously is not the goal. The point is to glean insights you didn’t have before and use them to improve your practices.
4. Do I have the talent to do this work for me?
Chances are, you are either not a data expert, or you don’t have time to be the data expert, so you need to find talent to serve in this role for you.
Get Started with the People
It’s okay if you didn’t answer yes to all of the questions above. In my opinion, the best way to begin down the path toward achieving a meaningful 360 view of your students is to start with the people.
As I mentioned, lack of talent is a common barrier to getting and effectively using student data. Data analytics is a hot area across every industrial sector, including higher education. Good people are worth their weight in gold and are in high demand. You should employ talent in three key areas:
1. Discipline expertise
It’s not about being a data geek; it’s about being a student geek. These people need to really understand your student experience, really understand your institution and really understand the questions you’re trying to answer.
2. Technical expertise
These people need to know how to build and use the system to get the at data for which you’re looking. This may involve reconfiguring your current technologies and/or evaluating new technology platforms that offer more integrated data out of the box.
3. Action expertise
Finally, you’ll need someone who knows how to take all of your great, integrated data and then actually DO things with it. This may be the same people you’ve employed for discipline expertise.
Get these people on board, or find them within your institution, and you’ll be well on your way to a true 360 view of your students.
Remember, data for data’s sake is not the point. You’re trying to use the data effectively to change your practice to be more effective as an institution. A composite profile of your students, achieved through data and analytics, lets you serve students better, identify their risk factors earlier, mitigate against those risk factors—all ultimately helping students and educators achieve success through higher persistence rates and graduation rates.