Dr. Cristi Ford (00:00):
Welcome to Teach and Learn, a podcast for curious educators, brought to you by D2L. I’m your host, Dr. Cristi Ford, VP of Academic Affairs at D2L. Every two weeks I get candid with some of the sharpest minds in the K through 20 space. We break down trending educational topics, discuss teaching strategies, and have frank conversations about the issues plaguing our schools and higher education institutions today. Whether it’s ed tech, personalized learning, virtual classrooms, or diversity inclusion, we’re going to cover it all. Sharpen your pencils, class is about to begin.
Listeners, welcome back. I’m really glad to have you with us here today. I have been thinking about this episode for quite some time, with the growing pressures from college professionals, to think about evidence of successful activities, programs and services. Data informed decision making must be part and parcel of the way higher education is constructed. And so, while it’s difficult to experiment and measure outcomes in educational settings, there are tools available to help us do this more effectively.
So, in today’s episode, we have a real treat for our listeners and I’m really, really excited to introduce you to our guest today. Amelia Parnell is Vice President for Research and Policy at the National Association for Student Personnel Administrators, also known as NASPA. She leads many of the association’s scholarly and advocacy focused activities. Dr. Parnell is a frequent speaker on topics of student affairs, college affordability, student learning outcomes, and institutional use of data and analytics. Dr. Parnell currently serves on the board of directors for EDUCAUSE and is an advisor to several other higher education organization.
She also hosts a podcast called Speaking of College, and she’s the author of a book that we’re going to spend most of my time with her today talking about. It’s entitled, You Are a Data Person: Strategies for Using Analytics on Campus. Amelia, thank you so much for joining me today.
Dr. Amelia Parnell (02:05):
It is my pleasure, and I’m looking forward to this conversation.
Dr. Cristi Ford (02:09):
So, as I thought about having you as a guest on this episode, I thought about the many conversations I have with institutions, and in talking with them, hear terms like data-driven decision making, continuous improvement. And it makes me think of now that we’re in this era where college professionals are looking for better ways to demonstrate the value of their work as well as build trust with stakeholders. And so this topic around data is such a relevant one that I really wanted to dig in and talk to you a little bit more about your book and your experiences as well. So I want to start with really asking you, what inspired you to write this book?
Dr. Amelia Parnell (02:49):
Well, thank you for the good framing question and I hope that as people listen to this, this will carry out throughout the rest of the discussion. The very first reason is that I wanted to do something that might change the narrative about how campuses, how individuals, how organizations should be using data and make it something that’s more positive, relatable, practical, and a bit inspiring. I say that not because I feel like the environment that we’re in right now is highly contentious and punitive, although that is part of it. I just hadn’t seen too many people talk about using data in ways that actually would entice somebody to want to do so. And so I kept hearing all these people say, “That sounds really good, Amelia, but it’s a matter of compliance. We just have to do it.” Insert name of accreditor or insert name of board of governors, insert name of whomever.
And it just felt like it was something that was very regular routine required and those things are necessary. But I thought that perhaps maybe a different take on why we should be using data one that would encourage somebody to say, “Okay, I realize I’m going to have to do this, but doesn’t have to be daunting.” Would be a good thing to add to the field. So I want to say that I certainly realized there have been other authors and other pundits and people who have spoken on the topic, but I wanted to add a different voice, one that might bring people together and change the narrative in a way that feels a little bit more positive. So, it’s not a positive spin, but a positive take on why we should use data. So ,the real reason after that was that just there were too many people who actually literally said, “I’m not a data person.” So, the title came to me because that was my response. No, you are a data person. And the book is to explain why.
Dr. Cristi Ford (04:22):
I love this and I love this asset-based approach to thinking and positive framing around data, as I work with institutions that think about educational interventions and the kinds of really fantastic work that we’re doing. I think that your book really speaks to that faculty member, that educator. And so, as I think about your book, how does it help educators use analytics in their courses, for instance?
Dr. Amelia Parnell (04:48):
I think in general; educators can look at it in real-time. So, the book itself is not intended to be a kind of one-time use thing. It’s intended to be something that you would consult over time. And so, in it are lots of practical approaches for which you would probably already be doing, one of which is to look at needs, processes and outcomes for example. And I’d imagine any educator, whether they’re teaching in-person live with students or online or some other configuration, they’re going to want to know is the instruction that they’re providing or this particular discussion resonating with students? Are they taking away the things that they hope they would? And so the book doesn’t necessarily offer so many new ideas that a faculty person would say, “I have not been doing any of these things and now I got this laundry list of things I got to try.”
But instead to say, how might you take a data informed approach to some of the routine things that you already do? So I can think of two really practical examples. One is the real-time polling, which is a real-time assessment of how students are progressing with the material. That can be as simple as a probably written something, write down the two takeaways you have. It could be more sophisticated with a polling system if you’d like. But the idea is that you would, in real time, be able to see how your pursuit of these learning outcomes is actually playing out. So you don’t actually have to wait until the end of the term or some formal grading or assessment to see so. So in the book, I’m saying that the assessment of needs is really critical. Now oftentimes we go straight to outcomes, which is did the student actually complete the class?
But this real-time assessment of what students are picking up or maybe leaving behind helps us understand what their ongoing needs are. So that would be the first one. And then the second, I think the use of analytics to figure out exactly what type of tailored advising students would benefit from. I think many students who are going through any type of credential pursuit are going to ask that question of themselves, “Am I on the right track? Am I truly making decisions about the courses I’m taking and how long it’s going to take me to get to the end?” Which is to complete the credential and hopefully leverage that for something more. They’re going to need some advice. And so to some extent, I think our use of analytics as instructors will help us provide more precise counseling, coaching advice to students. And that’s not something that you would probably lead with.
We oftentimes think about performance outcomes. How are they performing in the class? Are they getting the grades? But the outcome of that whole educational experience is that they want to leverage the credential for something later on down the road. And faculty, I think, are increasingly in roles that require them or students ask of them, “What do you think about this thing that I’m pursuing?” So that’s a long answer, but the short two takeaways are I think we can use analytics in the classroom space more so instructors to get a real-time sense of how students are progressing without waiting till the end of the term. And then second, a closer examination of our existing analytics will help us provide better advice to students as they’re making their critical decisions in term and beyond.
Dr. Cristi Ford (07:26):
So I really resonate with that in teaching classes myself. As I think about historically how I’ve divided my lectures, I’ve had the opportunity to use that real-time polling. And so couple that with the formulation that Eric Mazur teaches us around peer instruction and using concept tests, I use that real-time polling to then determine where I need to slice and spend more time in really being able to make sure that students are understanding the concepts that I’m presenting and in a very punitive low stakes assessment.
So I really appreciate you talking about the importance of doing that in real-time and not just focusing on the large big outcomes and those high stake assessments. So thank you for that. And the other piece that you mentioned as I’m thinking about the educational voice and thinking about your book, would you say that this book is really just targeted to educators? Is it for innovators? Is it for administrators? What is the target audience of those that would most benefit from your book?
Dr. Amelia Parnell (08:28):
Honestly, I would say anybody, and I know that’s a big group, but I would probably say anybody who currently works in a higher education setting. Now, it’s primarily the last part of the title says using analytics on campus. So it is a little bit more geared toward those who have roles that are actually affiliated with the campus. So on campus probably, could be on campus virtually or on campus in person, but even those who are enrolled that are a little bit adjacent to a campus, I think they could probably pick up something from it.
With one exception, I think the book probably for those who already have a title like data scientist or data analyst or a statistician, their understanding of the use of data will be different. It’ll be a different take for them. It will be heavily on the relational side, so I’m not excluding them, but let’s say chapter one where I’m going over common terms, what is artificial intelligence? What it’s predictive analytics? They may know those terms. So that’s the one portion of the book that I’d say those who have traditional roles could probably skip that chapter. But the rest of the book I think is appealing to everybody. So it is as much relational as it is tactical. And because of that, I think it appeals to everybody in a higher education space.
Dr. Cristi Ford (09:30):
And I think what I have gleaned from reading the book is it offers this common language and framework, so it provides an opportunity for fluency and to build capacity at every level so that if you read chapter one and now you’re talking across campus or talking with those folks that have data analysts in their title, you have a better understanding about what actually they’re talking about. So I would definitely agree with that for sure.
So, I want to talk a little bit about, as you’ve done your work and your background, you’ve spent a lot of time thinking about research. I’d really like to understand as you think about the benefits of using analytics in higher education today, how are they differing from where, maybe they were 10 or 15 years ago? What shifts have you seen? What kinds of trends?
Dr. Amelia Parnell (10:15):
I think maybe the biggest shift is probably the sophistication of what we’re doing. I think the primary purposes of using data and analytics probably haven’t changed very much, but the way we describe it, we oftentimes lead with the name of a product or a service. I won’t say it on the air because I would be remiss if I mentioned one platform and didn’t mention another. But I think the way we talk about it has evolved to probably more often than not have the mention of some catchy, particular, trendy something. But at the core, if I think if you strip all that away, the common purposes for which campuses use data are probably pretty consistent over time. Now, we may have advanced to something that takes us less time to do the analytical work, but at the core, I think there are a couple primary benefits of using that I think have been consistent over the past few decades.
The first is that we want to be able to determine the impact of what we’re doing. So ultimately we want students to come to us, pick up some learning and take that learning, translate into a credential, and then leverage that credential for additional study, gainful employment, so on and so forth. And so we need some information to show to what extent are we actually able to deliver on that? What’s the impact that we’re making with these programs and services and instruction? I don’t think that’s changed. The second is that once we figure that out, we want to know are we actually leveraging our resources to the highest level that we can? So are there places where efficiencies could be popping up here or inefficiencies? And so we need some data that show things everything from utilization of resources to the cost of certain things. And so it’s expensive to the student to attend. It’s even more expensive to the campus itself to operate. And so we need some data to show that our resources are being used appropriately.
And then third, the first two seem very programmatic, very essential to which you would normally do. But the third, and this is the part that excites me the most, I think it opens up conversation for the future and the things that are possible. So if we know that we have delivered hopefully on the promise of what we’re trying to do educationally and socially and environmentally, if we know that we’ve leveraged our resources as best we can, now we got some room to grow, what are some areas of opportunity? How can we transform the higher education enterprise?
And I think analytics can inform some of that as well. So if I’m looking back to 1970, I think all the schools that were around, they still want to be efficient with resources. They still want to provide the high-quality educational experiences students need. But then they were looking to 1980, and people in 1980 were looking to 1990. So I just dated myself. Ooh, that’s a long time ago. But still, I think that if we’re willing to look ahead to even 2050, most campuses and organizations in higher ed would have those same goals.
Dr. Cristi Ford (12:35):
So I would completely agree with you. One of the things that I think is interesting in the last 10 or 15 years is we’ve moved to this place of continuous improvement and feedback loops and really thinking about not just having that one time effect, being able to assess something and then say, “Okay, we’re doing great, put it on the shelf.”
So it has been interesting to me to see how sophisticated analytics have become to really make sure that are we still providing the service to the right audience and how do we need to continue to do that well. And so I know you talk a lot about learning outcomes and some of the talks that you give. And so I’m wondering, as we think about analytics as it relates to educators, how can educators use analytics to be able to better show efficacy around the learning outcomes of their courses?
Dr. Amelia Parnell (13:21):
Well, I think it always starts with the discussion of probably at the campus level and the department level, what are the learning outcomes that you want to pursue? So I’ve had some really great opportunities to talk with campuses who might say, “Hey, we actually haven’t updated our learning outcomes in a long time.”
And so I think it always starts with a key discussion that regardless of which student is taking which courses and which department or major, are there any essential learning outcomes for the whole campus that you would say any student graduating from NASPA University should be able to do the following things. And typically when we look at that, they’re going to be as broad as communication, problem solving, critical thinking, things of that sort. After you boil that down to something that should be specific to those who are majoring in architecture or nursing or biology, that’s where the department level discussion of learning outcomes and competencies and skills starts to get to be more interesting.
And so I would say we could do some things that are very common. Let’s attach certain learning outcomes of certain courses, and if we have certain assignments, those map back to the learning outcomes. And then we can do assessments of that learning by testing and things of that sort. And that’s part of it. But I’d like to think that in the future we’re going to be looking at this more holistically. So I’ll give a sidebar example for the University of South Carolina, it’s going to be a shameless plug for students getting involved outside the classroom, because typically when we say learning outcomes, we think of something that would happen in a traditional professor to student type setting. And it does happen there. But the reason why I started mentioning it at the top to say what’s their campus level of personal learning outcomes, is because typically a student is going to be learning everywhere.
And so with that in mind, this university, my friend’s there who I’ve had a chance to know for many, many years, they wanted to do a bit of an assessment of learning outcomes type of project where they said, “We have all these programs and services and activities and interactions that are happening outside the classroom. Wouldn’t it be nice if we could assess the extent to which some of these learning outcomes for the campus are actually being pursued in these spaces?”
And so what started off as an assessment of learning and assessment in general of their programs and services and student services, turned into, shouldn’t we capture this though? So we could have a whole other separate conversation about the evolution of learning records and how to go beyond the transcript. But this university approach led to them saying, “Okay, we have actually now dashboards. We can show the number of different types of engagements that pursue certain learning outcomes, and we can see the extent to which students are engaging in each of those.”
So the dashboard approach could make some nervous and say, not another dashboard that I got to filter and sort through, but I like the idea that it’s intentionally mapped back to the learning outcomes with the goal of even documenting that, so that the end of the term or the end of the semester or the end of the whole experience, students can say, “This is what I know and what I can do, and my campus has an actual record of that, and we have now trend data over time to say what are the most impactful out of classroom and in classroom experiences that lead to the attainment of these learning outcomes.”
So before you could even get to something at that level, I do think it does start with the actual sitting down and saying, what are going to be our most essential learning outcomes? And then you’re going to have to probably select some type of framework, but embedded in all those decisions will be plenty of opportunities to collect data. And it doesn’t have to be super daunting or cumbersome, just want something that’s going to show progress over time. And you can even be as specific as to show subgroups of students that are engaging. And if you see certain students not engaging or not picking it up, then you can plan your strategy around it. So as you can see, I got a lot to say. You’re right, I haven’t talked about this for a while, so I wove a lot together in that answer.
Dr. Cristi Ford (16:31):
No, that was really, really brilliant. And I think you also wove together in terms of new trends and analytics, talking about dashboards and data visualizations and the importance of thinking about how do you bring in. And so what we work with a couple institutions that are bringing in their LMS data from Brightspace and really being able to see the larger holistic picture, what they couldn’t do in 1980. I’ll date myself with you back then, but was not that sophisticated. So I really appreciate that approach. I guess one follow up is now that we’re creating data fluency and thinking about the fact that you’re telling us in your book that everyone is a data person, how do we help educators understand the right data to improve learning outcomes?
Dr. Amelia Parnell (17:16):
That’s a good question. Before I answer it, I want to maybe offer, it’s almost like a Cliff Notes piece of the book. So for people who are less familiar, and this is why someone can say, “Okay, yeah, this is interesting, but tell me why you think everybody is a data person?” I want to provide a spoiler and say in the book, I offer up what is something I refer to as the data identity framework. And I make the case that every professional, regardless of their role or what they do or where they work, they have some extent common use of six core abilities, one of which is research and analysis. So we can’t call this a data related book without saying that everybody should have some level of frequent use of data and information. So by research analysis, that’s component number one of six. It just means that you have the ability to select the appropriate methodology, so to know when it should be a survey or when it should be a focus group.
But there are five other things that I think are essential. I’ll try to be brief with it, but five other things that I think are essential for any professional to keep in mind to make a very holistic set of decisions using data. One of them is curiosity and inquiry. So being able to ask a clear question. I can’t tell you the number of times I talked to professionals who’ve said, “I just want to know the number of transfer students who are majoring in biology, and then we can make a decision.” And then they get the data and they’re like, “Oh, wait a minute, I wasn’t expecting that. I think what I really want to know is the number of transfer students in chemistry.” And it’s like by the time you get to the 10th question, whoever’s providing that data is like, “Now come on, give me a break. What do you really want to know?”
So if someone has the ability to kind of cut through all the noise and at some point synthesize things to a point where you can say, “Here is what we most want to know.” That’s something to honor. It saves you time, it avoids scope creep, there are a lot of other reasons. So I want to honor that. Of course, add that to research and analysis. The third is communication and consultation. We’ve had a lot of moments where someone has done some really lengthy analysis and then they hand you that report that’s 15 pages long, single space, and you’re like, “Ooh, there’s a lot in here.” I just don’t know how to distill this out to the most important pieces. But if someone all the time is saying, “I think the most important pieces that we need to know are this, this, and this.” That’s something to celebrate as well, because data are there, but you have to put it to use.
And so to know what course of action to take, you need someone to translate some things for you. Strategy and planning, since I’m on that topic, would be another one. So you have all types of data, we’ll probably talk about that later, that flow across the campus. There’s something to be said about being able to put a course of action together. How many people do we need? What resources do we have, financially and otherwise, technology resources. So data will inform the decision, but you still got to put it in action. So if you’re someone who likes strategy and planning, that’s a part of it too. And then my last two are where going to lead into the answer to your question, campus context and industry context. And what I mean by campus context is when you’re working on a campus, and you could maybe translate this, let’s say you’re in an organization that’s adjacent to a campus.
Let’s say you’re working for D2L, but you’ve been at D2L for three or four years and someone says, “Let’s try this.” You now can say, “I’ve been here long enough to see certain trends come and go, to see certain initiatives and strategies come into play.” You can bring that to the conversation and hopefully save some time if in fact what you’re trying to pursue has already been done. You could even also check the parking lot for some ideas that got going but never took off after a while. So campus context is huge. And then industry context is just having a connection to places and spaces that are outside of where you typically work. So if you are part of a national organization, I won’t say shameless plug for NASPA, but even a part of a learning community at D2L, anytime you have a chance to talk with colleagues who have similar roles or in different spaces and comparing contrasts which you’re doing, that’s why I say everybody’s a data person because we all use a little bit of those things at all times.
And those are all essential to making a data informed decision. So with that in mind, you ask about the right data and what I would say, I don’t perceive it as right or wrong data. I think of it as data that has the right context. And so one way to avoid using the wrong data, or I’d say data without the right context, is to check your assumptions, and most often check your assumptions with colleagues. And when appropriate, check your assumptions with students themselves, especially if you’re using data to make decisions about what students need. So to me, there’s always a chance to say that was the wrong data. It might in fact actually be the right data, but the wrong question. So I would say it’s better to check your assumptions and make sure you have the right context before proceeding with what you’re using.
Dr. Cristi Ford (21:16):
I really appreciate the framing. And listeners, we got a sneak peak at the six framing principles, and I really appreciate how comprehensive they are. I really resonated with the second one. It reminds me of doing some innovation work. And the first question we asked educators is, what is the problem you’re trying to solve? So if we could figure out the root cause, then we could really identify the right question, because as you mentioned, data for data’s sake, you’re going to have more questions once you get the data. And so I really appreciated that framing. That was really, really helpful for us to share with our listeners. I guess I want to know when you’re thinking about, and you talked about institutions, typically collect and analyze all kinds of data. What types of data do we typically or do you typically see institutions utilizing and analyzing?
Dr. Amelia Parnell (22:10):
Oh, a lot. So think of like a buffet, if you will. So I’ll give you the ones that come to mind as probably the most frequently used, but this will not be an exhaustive list. But most campuses to some extent probably use all of these types of data and more. So the first, student data, you can’t get around it. So we always have questions about how students are performing academically, how are they doing financially, to what extent are they actually engaged in programs and services, activities, if you will. We even can track their location. We can tell to what extent there are certain behaviors that they’re taking that are consistent or not consistent. So all types of data related to students, what they know, what they’re doing, how they’re progressing, all those types of things.
When you look on the operational side of campuses, lots of human resources data. So I’d say the biggest line item for most campuses is probably going to be human resources and personnel. So you got all types of data about the employees and staff who work there, salaries, performance management data, all types of things. And not all this data is public facing, but this data that all are being used to inform decisions. Financial data. Colleges are expensive. These are multi-million dollar enterprises. So all types of data relate to revenue or expenses, and some of that ties into enrollment management. So a lot going on there. And then I’ll mention maybe two that are maybe less visible, but just as important as the ones I already mentioned, safety and security data. So everything from clarity reporting to campus climate surveys, so just all types of things that relate to the safety and security of students and the whole campus community.
And then lastly, use of your resources, facilities. So space utilization if you’re on campus, if you’re off campus, the use of your technology systems, online libraries, other campus platforms, learning management system data, things like that. So there’s a lot in there, but as you can tell, I wouldn’t say that there’s any one single owner of all of those pieces of data, though they all do fit into different organizational chart areas. I think those are all pieces of data that we all care about to some degree and have to make our biggest decisions around. Good question.
Dr. Cristi Ford (24:02):
I appreciate that because as I think about senior leaders, that’s why in institutional research is important, right? They’re shacking and connecting all of those data points. I do find that I’m hearing more and more from institutions that are serving historically marginalized communities, that are serving communities that are more contemporary learners, that there is a need to reconceptualize what we’re requesting, what we’re measuring, and how we’re doing that. We’ve talked with Dr. Tia McNair recently in another conversation about this. So I just wonder, as you think about where you see an opportunity for improvement in growth, are there different kinds of data points that we are now starting to see being considered that historically have maybe not been there, or maybe we’re redefining the way in which we measure those things?
Dr. Amelia Parnell (24:58):
There are a lot of challenges I would say, that are associated with collecting all these different types of data. And so part of what I’ll describe with that in mind, we’ll touch on some of your answer and then I’ll provide what I think is probably an area for the future, probably if this is going to be like the sound bite, the most underdressed area with regard to data that I would say we would be right for opportunity with.
So when I think about what you just described, one of the biggest places for improvement I think, is the inherent biases that can be there. So we’ve had lots of conversations about predictive analytics, and that’s a really great tool. It’s not the singular way to make decisions, but I can see how we get more sophisticated with our use of data. And we would say an algorithm shows us that Amelia has a 40% likelihood of persisting if we do the following things and then you prescribe some interventions.
I get that. But when you’re doing that type of decision making, I think we have to remember that the model is only as good as the context that we put around it. And so there are no two students who are exactly alike. They may be similar on paper profile in terms of race and ethnicity and other demographic characteristics, but they don’t have the same friends, they don’t have the same other advisors. All those things that play into how they would approach college.
So on the flip side, when we are looking at information results of analyses and we’re making assumptions, we all carry some biases. Well, I’ve never seen that. Let’s say the data suggests that those students in that chemistry class are really struggling within the midterms time of the semester. I’ve never seen that in my class. That doesn’t seem to resonate with me.
And so that use of that singular perspective or you’re bringing your ideas about what should be done, I said we always have to check our assumptions and really keep an eye out for inherent biases that are baked into everything that we do. So I probably say that’s the biggest challenge after that, governance, is very, very tempting to say if we have a smaller percentage of Black students on the campus, we really, really, really want to talk to them all the time. So we want to survey them all the time. We want to invite them to campus lunch and learns, we want to give them pizza and things like that. That’s all fine and good, but at some point someone’s going to say, “I need that data. I want that information that you got. I wasn’t able to come to lunch and learn, but can you give it to me?”
Now, that’s a simple example of how you probably could manage it. But what happens when you have everybody on the campus who all want the same information and for different purposes? So another tripping up point that I see happens a lot is this area of governance. It’s not the most attractive part of the conversation, but somebody’s going to have to determine who gets access to what degree and how often, that type of thing. Because if not addressed, you get into the third challenge, which is privacy. And on the most egregious end, you hear about ransom issues where someone would hack into your system, not necessarily talking about that, I’m talking about the appropriate safeguarding of students information. So what happens when you have health records? Lots of questions come up about whether professors should see students financial position data, things like that. Or should an advisor of another type have access to health records?
If they think they want to provide a holistic approach, we know everything. Maybe we should, maybe we shouldn’t. It’s to be determined and that that’s not enough. There are all the relational pieces that fit into this. So what happens if you have those two people on a campus who have programs that they’re running to address the needs of historically marginalized populations, but they’re in competition with each other? Those two individuals are competing, not necessarily by their own design, but someone has told them, whichever one of your programs does the best, you get more funding.
And so now you have data that’s being leveraged for things that involve politics and competition and even a little bit of denial depending on how things are going. So I like this topic. It’s one of the most attractive topics in all of higher education. It’s evergreen. We’re going to be talking about this for the next 25, 30 years or more.
However, it’s still highly relational, highly related to people using this information. And anytime you have that, I think there’s a tremendous opportunity for misuse, whether intentional or not, and mishaps that can happen as a result of misuse. So with that in mind, you asked me what do I see as the future? Something as an opportunity. The biggest area of opportunity in my humble opinion, is data that relate to the strategic use of communications. And I mean that sincerely to say in a situation where you have thousands of students all going through different experiences at the same time, meeting different people, learning different things, it’s essential that they have the best communication possible in both directions. So they know who to reach out to for different things, and people reaching out to them have the right information. And that’s just the student facing side. Imagine this cadre of hundreds of employees, faculty, administrators, staff who need to have consistent and effective communication about what’s going on.
And we’ve seen this from a couple different vantage points. AB testing, this message A, work better than message B. Is a certain time of day the right time? Should we talk to people on social media? Things like that. That’s scratching the surface. But there’s a strategy around this, you know what I mean? Can we do more with less? And we always hear that students don’t read their email. And my thinking is that if they’re similar to me, I read the email, but sometimes I’m not ready to respond or sometimes I don’t know how to respond. That’s me in my personal inbox right now. There are a number of unread messages, not because I never read them, it’s because I read them and I marked them back as unread, because I said, “I can’t deal with this right now.” It’s too many in here.
Some of these messages have both a request and information in, I don’t know where to start. I’ll sort this out when I have time. So imagine, I have the luxury of being able to decide when I want to do that. And what’s at stake is that maybe that inquiry gets responded to two days later. But for a student who is in the middle of preparing for an exam and they also have to register for class for the next term, and they also want to be a part of this living learning community, and they also want to do this service learning experience, and they also just want to go to the social thing that’s happening the following week, if those messages are not strategically delivered in a certain way, they can feel overwhelmed. They get to say, “I’m not going to do anything until I can sort this out.”
And sometimes time is of the essence. And so I wish I could give you a solution. But I think analytics is going to be woven into that. So I don’t know that we necessarily need a whole brand new section of campuses that are specifically charged with strategic communication. So much as we need a strategy that is data informed that we can have as a nimble way to improve something that is very, very essential to people feel like they’re connected, engaged, and getting the most they can from the experience both on the student side and the personnel side. So that’s my sound bite, but that’s what I’d like to do in my spare time if I had time for it.
Dr. Cristi Ford (30:59):
On top of recording your podcast.
Dr. Amelia Parnell (31:01):
On top of recording the podcast and talking to you and everything else, all the things.
Dr. Cristi Ford (31:06):
So two things really resonate with me. One, the data governance piece, I think that is critical. Even when you talked about oversampled students, right? If you had data governance and we all knew who was surveying whom at what point in time, who owned surveys, who’s responsible for collection of that? I think all of that resonated with me when you talked about that piece. And then the second piece around strategic communications, I also wonder, could we use the data that we do have to inform personas?
Dr. Amelia Parnell (31:36):
Dr. Cristi Ford (31:37):
And as we create user profiles and think about the kinds of students we serve, what we know about them, what we know about their success and failure, to your point, not in a way that predictive analytics may allow us just to categorize everybody, but it may give us a fresh start to really think about how to make sure we’re not overloading the students and the cognitive load so great around that.
So I’m going to hold you to that. I think we’re going to be seeing more of that coming forward for sure. One of the things I’m thinking about at D2L, we focus in higher education, K12 and corporate. And as I was reading your book, there seem to be implications for other sectors of K12 and corporate partners. I mean, would you think that corporate leaders should be tracking data on campus partnerships, productivity or learning organizations, that side of the house at big corporations? If so, what kind of data would be most useful in those two sectors or those kinds of use cases?
Dr. Amelia Parnell (32:39):
So much I can say, but the short answer is yes. I could totally see even in isolated fashion. If there’s a corporation that is saying, “How can we better leverage analytics and even have a little bit of a dotted line to higher ed?” Absolutely, they have to do it for so many reasons. I think I’ve bought into higher ed, I’ve bought into the idea that college is worth it. But I also understand there will be some individuals who say, “The traditional college experience is not for me. I’d like to see what my organization can provide for me in terms of professional development and learning.” So all the same things we talk about in a campus setting, I could see in a corporation that wants to provide additional professional development and training and learning should be asking the same questions. What are going to be our learning outcomes for competencies that are attached to these jobs?
How are we going to ensure that whatever we invest in is going to get that return for the individual? So that’s just them operating in a bubble, never connecting with us. I could even see partnerships where they say,” We can provide some of it, but we want to also invest in professionals getting the rest of that from a campus.” That two-way discussion of everything from learning outcomes to the platforms you use and all that kind of stuff. There’s plenty of opportunity there. I could even see to some degree a campus on the flip side saying, “There are certain things that are outside the scope of what we can provide with our existing resources.” Public-private partnerships have been exploding equally as much now as they have been in the last five years. And so trying to fill a need through corporate partnerships for things that maybe the campus doesn’t quite have capacity to do.
Very, very common I said, but I think is an important piece there for them to use an assessment of their own needs and whatever processes they’re using back to that need, process, outcomes thing, to ask the corporations to maybe be a little honest about what they could provide, and also have the campuses be honest about what they really need. And so underlying all of that, regardless of whether it’s a corporation themselves saying, “Hey, we want to do our own internal learning agenda.” Whether it’s that they want to supplement what a campus is doing or vice versa. There has to be tremendous amount of trust there. And I know that, honestly, I think anything is possible. Anytime I’m talking about partnerships. I think once you have the need and what’s available there, it’s possible now it makes you some time to get it going and figure out what is.
But that trust piece is huge. I don’t see a situation in which most partnerships don’t work unless something happened where the one side doesn’t trust the other. Obviously communication woven into all of that. To make it even more specific, when you mentioned K12 and higher ed and corporations, the first thing that came to mind for me is I got to go back to the job attainment goal. The idea that regardless of where someone is entering into advanced education, in many cases they’re pursuing this because they want to advance in their career. And so if we’re looking for an intersecting point for all three pieces, the students who choose after high school did not go to college, they’re probably going to work. The students who choose to go to college, many of them are trying to attain a job. Those who are already in the workforce thinking about going back to college because they want to move up in their career.
So if we can’t find any other point of intersection to start from, I would say any data information that relates to preparation of someone to attain skills and competencies for the pursuit of further employment, I think would be a sweet spot to get started with. Once you have that open door, you can get even more sophisticated with other questions, whether you want to do it on your own or in partnership with each other. So I feel like I got nothing but long-winded answers to your questions, Cristi.
Dr. Cristi Ford (35:48):
No, this is great though. I mean, the listeners can’t hear me, but I’m nodding my head, I’m smiling. I really resonate with all you offer here. I’m going to go a little bit off script here-
Dr. Amelia Parnell (35:59):
Dr. Cristi Ford (36:00):
I want to just ask you, as we think about what all of the listeners have heard so far, is there a call to action or a lesson learned that you would offer to listeners that for them to take away with them as they hear this episode today?
Dr. Amelia Parnell (36:15):
Yes. Top of mind I have two. One, don’t be scared to get started. I think there’s a tremendous need for accountability and for assessment. And we can’t be loose handed with these conversations with data, but I hear sometimes people, they’re so concerned about not getting the data right or not getting the analysis as comprehensive as they need to be, that they take forever to get started. So again, I’m not saying just be sloppy with it, but I am saying maybe just get started.
Now on the flip side of that, don’t rush, don’t get so started with it that you want to do everything quickly, but take your time with it, get into it, and invite some colleagues to the conversation and just see what you can find. There are certainly aspects of data collection analysis that have to be precise. We’re talking about enrollment.
We can’t say, “Oh, we got plus or minus a thousand students.” No, I need to know exactly how many students we have. But if we’re talking about a program or a service and we say, how’s it going? I think we can say it’s going okay, or it’s going fairly well or it’s going great. And if the hesitation is like, “Well, I’m not really sure what data information I need.” Take that time to ask that question over and over and over again with the colleague, but just get started with the process. So that’s probably my first thing. Because it kind of goes with that early statement earlier. Because I said, “Hey, I hope that throughout this conversation it feels like something that’s very just additive.” You know what I mean? Asset based. And then the second is make a friend. I find that some of the best conversations about data and how to make decisions come from a conversation with a colleague and preferably somebody that you don’t typically work with, because chances are the same types of pressures if you feel them, or goals and objectives.
Back to your question about what’s changed over time, I bet a colleague is dealing with something similar. It may not be in the same department, but they’re probably trying to answer similar questions. And something about having a network of colleagues that are tackling big data questions makes the work feel a little bit easier to manage. And so I can’t tell you the number of people that I talked to, even beyond the 40. I have 40 quotes in the book from 40 interviews that I did. But even beyond that, I had so many more conversations. It made the conversation richer. And I think it made me a better writer of the landscape of what’s going on because I had all those perspectives. And I think that’s not unique to me. I think anyone who is trying to leverage data to greater in a greater degree would benefit from following a friend or making a new friend. So those are my two pieces of advice and takeaways advice.
Dr. Cristi Ford (38:28):
Well, and with that, Dr. Parnell, I’m glad I’ve made you as a friend. I’m telling you right. I have appreciated learning from your wisdom, reading your book, listening to your podcast. I guess I want to ask one final question in terms of, I know you just got off of an NASPA conference. But what’s next for you? What is something else that you could share with our listeners that’s coming up and coming down the pike for you?
Dr. Amelia Parnell (38:52):
On a personal, real personal note, with every month that my new puppy gets older, I feel more accomplished. And if you want to know if I ever make data informed decisions, practical, I do everything from Google. Can the puppy eat apples to how many times per day should the puppy go out? Things like that. So I’m using data all the time. I log all of his activities every day. Because I’m a nerd like that. So on a personal level, what’s next? I’m looking forward to his first birthday in June, so I can feel like, Hey, I’ve reached a milestone. I’ve been able to reach this thing.
Dr. Cristi Ford (39:20):
Dr. Amelia Parnell (39:21):
Yeah. Yeah. I’ve survived. And he survived, literally. Throughout the rest of the year I got some conferences I’m going to, some speaking engagements, just really some networking. I think coming out of the pandemic 2023 has felt like the first year that I can actually be a little bit more comfortable seeing people in person.
So I’m mostly excited about that. I’m just thrilled that people still find the book interesting enough. So I’m always having opportunity to talk and share with colleagues on that front. The podcast, so shameless spot, good for that. When the book took off, the podcast slowed down, and then I got the puppy and the podcast slowed down even more. So you were recently a guest on my podcast, but I’m really looking forward to getting that out to more frequent cadence. So I love these conversations where we speak mostly to higher ed professionals, but that is my labor of love and my project to give back to the community. So speaking of college, I feel like I’m about to give you a commercial. It’s intended to be, for those of us who work in higher ed who’ve gotten that question like, “Hey, you work on a college campus or you work in higher ed. My neighbor’s cousin has some questions about college. They’re thinking about going.”
And so I think to myself, if they didn’t have access to us, where would they go for that information? And so I wanted to create a show that was relatable and it provided trustworthy answers to college questions for an everyday audience. So later this year, by the time we get to December, puppies should be thriving. Okay. I should have connected with more of my friends like you and have more of these episodes, this podcast, so I can reflect back on and get back to the public because it makes me feel like I’m really able to make a difference with all I’ve been afforded the opportunity to do.
Dr. Cristi Ford (40:49):
This is so wonderful, Amelia, I really appreciate you taking the time and spending this hour with us and having this conversation. We will make sure to plug the podcast, make sure that our listeners know where to get the book. And again, thanks so much for joining me today.
Dr. Amelia Parnell (41:04):
Yeah, this was lovely. I enjoyed it.
Dr. Cristi Ford (41:07):
You’ve been listening to Teach and Learn, a podcast for curious educators. This episode was produced by D2L, a global learning innovation company, helping organizations reshape the future of educational work. To learn more about our solutions for both K through 20 and corporate institutions, please visit www.d2l.com. You can also find us on LinkedIn, Twitter, and Instagram. And remember to hit that subscribe button so you can stay up to date with all new episodes. Thanks for joining us, and until next time, school’s out.
Resources Discussed in the Episode
- Buy Dr. Parnell’s book, ‘You Are a Data Person: Strategies for Using Analytics on Campus.’
- Listen to ‘Speaking of College’, Dr. Parnell’s podcast
- Listen to Dr. Cristi Ford speaking on an episode of ‘Speaking of College.’
- Learn more about Dr. Amelia Parnell’s other books
- Follow Dr. Parnell on Twitter
- Learn more about Dr. Cristi Ford and the Teaching and Learning Studio