Abstract
Residential Education teams create powerful experiences, yet we often struggle to assess and communicate our impact in ways that influence decisions. In a time of tighter budgets and increased scrutiny, showing clear value has become essential. This session explores how building a culture of assessment, supported by intentional data visualization, can highlight outcomes, strengthen storytelling, and inspire stakeholders to support the work. Participants will learn practical strategies to present data with clarity, reveal key insights, and connect programs to student success. Attendees will leave with tools to craft data stories that guide decisions and elevate the visibility of Residential Education.
Outcomes
- Participants will be able to apply foundational strategies for collecting, organizing, and visualizing assessment data in ways that clearly communicate program outcomes and student success within Residential Education.
- Participants will be able to create compelling data stories that use purposeful visuals and narrative framing to influence stakeholders, support resource advocacy, and strengthen the visibility of Residential Education contributions.
Presenters
- Shigeo J. Iwamiya (he/him), Director of Residential Education, University of Southern California
Date Of Recording: 3/20/26
Watch the Video:
Links and References:
Roompact produces a monthly series of free webinars on residence life practice. Live webinars are exclusive to Roompact schools, but recordings of most webinars are made publicly available for the benefit of all.
Transcript:
Amanda Knerr:
All right. Well, good afternoon everyone and welcome to today’s Roompact webinar. We’re so glad that you could join us. My name is Amanda Knerr and I’ll be your host for today’s session.
Before we get started, just a few quick announcements and reminders. First of all, we’ve launched a new feature that unlocks all of our content from webinars like this one to blog content and podcast episodes. Ask Roommate allows you to search our content with traditional search AI powered search or browse by topic area. You can find Ask Roompact throughout our site or just go to easy to remember askroompact.com.
Secondly, if you’re job searching, don’t forget to check out our jobs at Roompact Schools page. You can find openings for positions at various levels and information on how to apply.
Finally, we hope to see you in Anaheim. Registration and program proposals are now open for our R2, the Roompact and Residence Life Conference in October in Anaheim. All details can be found in our link at top of the main homepage.
Now, just a few housekeeping items before we get started. Today’s session is being recorded and the recording will be available on the Roompact website within just a few days. All participants are currently muted to help minimize background noise. If you experience any technical difficulties, please use the chat feature to let me know, and I’ll do my best to assist you and make sure it gets working properly. Throughout the session, feel free to submit your questions or comments in the chat. I’ll be monitoring it throughout the webinar and we’ll make sure our presenter has time to address your questions during or after the presentation.
Now, I’m super excited to introduce today’s featured speaker, Shigeo, Director of Residential Education at the University of Southern California. Shigeo will be leading today’s session, From Data to Decisions: The Power of Clear Visualization.
In this session, he will helping us to explore how building a culture of assessment supported by intentional data visualization can highlight outcomes, strengthen storytelling, and inspire stakeholders to support their work. Thank you so much for coming to present today your expertise with the Roompact community. The floor is all yours.
Shigeo Iwamiya:
Thank you. Thank you, thank you, thank you. So I’m really excited to be here today, and I am happy to share some of the things that I’ve been working on a little bit through this channel that is the Roompact webinars. I’m really excited to be here. I’m Shigeo, the Executive Director now, that just happened literally a couple weeks ago, I’m Executive Director of Residential Education at the University of Southern California here in sunny, very hot Los Angeles. Apologies to people that are dealing with these winter conditions throughout the country, but it is 90 degrees over here.
So I wanted to introduce myself a little bit more and talk about the role that I play sometimes in this presentation or anything like that. I call myself a narrative technologist in the sense of I use a lot of data to talk about what it is that we are thinking about doing. One of my top strengths and strength flex is a strategic approach or learner or futuristic. So a lot of these things that I think about are coming out in this way, and I use a lot of data to think about that.
I also use a lot of art forms or pictures or digital demonstrations to be able to talk about what I do and build things out a little bit more. Probably my background is slightly in graphic design, so that probably comes out a little bit more in a far more intentional way. And I like telling stories. That’s where the narrative technologist comes in. I love to convert these data’s art pieces into stories and figure out why or how we can improve lives on campus. And then of course, I use the technology mediums like this to present and talk about how is it that we can shape certain futures, change the world, all those other kind of things, and hopefully spark ideas because I really want people to have these moments and then say, “ha, I want to try to create something, a different kind of experience.” So turn on those light bulbs a little bit more too.
The presentation that I’m about to do is separate it into six chapters, and it talks slowly into what is data, what is visualization, and how we can apply certain things to learning. And I’ll share some examples here at USC and general examples. And so if you have any examples that you would like to share or even have questions, feel free to put them in the chat or at some points these chapter breaks will come up and then we’ll be able to answer some of these questions as we go as well.
So without further ado, chapter one, we are going to move forward, The Comfortable Place. We talk about data in a really comfortable way sometimes, and I want to address the way that we talk about data currently in the student world of student life. It’s important to think about what is really data? Because it’s millions and millions of data points that we start thinking about, what is it that we are collecting? Why is it we’re collecting? What is information look like?
So imagine yourself in an environment where all these data points exist all over the place where it could be no rhyme or reason as to why we’re collecting things. There are thousands and thousands of touchpoints that we get to interact with students or maybe staff or maybe assessment points or opportunities for some of the students’ worst moments, best moments. But oftentimes in the world that we produce work, it kind of looks like this. It’s quite literally scattered all over the place and maybe potentially hard to understand because of the fact that we do a great job collecting data all the time. We probably don’t even know we’re collecting data sometimes if we’re collecting data. And it kind of looks unfortunately like this, and there’s different colors, different points, all over the place, different layers perhaps. And I want to go through this sort of rigmarole to talk about, what is it that we do? How do we organize this a little bit more and how do we convert that to stories?
For example, some of the data that we talk about, these are numbers that are made up, but at the same time, sometimes over any academic year, we might say, “Oh, we had 5,892 duty calls.” What does that really mean? What does it talk about, meaning … Okay, a lot of time you’re vice president of student affairs or somebody might say, “Hey, how many duty calls do we have?” And we’re calculating all the things and documenting all the incident reports and calculating what that means, but what do these duty calls really mean?
For example, another thing that we always track is program attendance. Even if you say that you had 8,752 programming attendants, what does that really mean? What can we really didact from that? We do a really good job of creating these still two-dimensional statistical images that say, “At any given time, this is the snapshot of what we collect,” but it doesn’t go in any deeper than that. A lot of times, some schools are doing some good work and great work around this, but most schools do a great job of collecting information and maybe showcasing a snapshot of what that might look like, but nothing more than that.
Care reports, another example. In a year, you may say anywhere from 500 to 1,000 care reports are collected, 873, as this example would say, but what does that really mean? What exactly are we measuring when it comes to the number of care reports or what are we talking … What exactly can we draw from this?
A lot of times we talk about training assessments. Sure, on a 5-point scale, we had a 4.6 training assessment response. Really, what really is that storytelling? Really need to think about what is data and why do we collect it and what are we trying to do with that data? Collecting data is just literally collecting numbers, collecting data points, collecting maybe even conversations of what people are doing. Platforms like Roompact are great at tabulating a lot of this information, like for example, intentional conversations or program trackers or roommate conversations. And being able to collect all that information, it’s great, but what are you really doing with that? And in fact, if you’re not doing anything with that, what is it that now we might need to think about because what is the responsibility of collecting the data and not being able to do anything from it?
So let’s think about all these data points that we’re collecting today and over the next hour or so to talk about what we could potentially do with all that information and talk about how we’re going to share some of these stories. So for example, one of the examples here is this is what we do at RA USC. Here’s our RA programs tracking the number of attendance and number of programs that we have. But if somebody just presented slides like this or even Excel charts like this, this is an all too familiar story that we often have, it doesn’t do anything for us other than the fact that you could say, “Oh, okay, New North,” which is one of our residential colleges, “Had 373 students attending this Fall’s programs.” Well, really, what does that mean? In comparison, McCarthy had 1,704 students participating in programs, but really what does that mean?
Our brains aren’t designed to process raw tables that correctly. So in order for us to create a storyline behind it, this is where the data visualization comes in. We really need to think about, how do we convert some of our everyday mundane data charts and convert them into stories or even visual charts so that people can understand and you can intentionally tell a story that’s far more powerful to be able to convince your supervisors, convince your campus partners, convince your students, RAs to say, “Hey, what you’re doing is mattering.” And to be able to demonstrate the value that residential education or residents’ life is having on our student experiences.
So that is where our chapter one ends. Let’s talk about the data and the importance of it, but let’s take it to chapter two. I am also not monitoring the chats, so if anybody does have a question, feel free to pop it in. I’m happy to do that anytime you would like. So chapter two is The Sunken Place. This is a little bit of a homage to the Get Out movie a little bit. So let’s go down deep into the sunken place and think about what is it that our data ends up doing if it’s in these Excel charts.
We’re collecting so much information. For example, some of the information that we would collect, entry swipes, training assessments, program locations, where did it happen, attendance, formal assessments, vandalism, intentional conversations, room changes, mini assessments, program spending, incident reports, medical transports, training attendance, duty calls, case sensitivity or severity, conduct cases, care reports, unique attendance. The list goes on and on and on. Roommate conflicts, newsletter open rates, housing retention rates, student leader applications, dining swipes.
I mean, talk about every little point that a student is navigating campus life, we are collecting information. It’s living so many places at so many times, whether it’s Roompact, Maxient, Advocate, internal websites, internal dashboards, internal swipe records, whatever that may look like, you’re collecting data all the time. When you’re having a one-to-one conversation with your RA, the RA talking about all the things that they’re doing, that is data, it’s quantitative, but nonetheless, it’s data that you’re collecting all the time in order to be an effective supervisor. How are you running your staff meetings? How are you running your student staff leadership one-to-one meetings? Everything that we’re doing is data, but we’re not doing a great job of converting it.
So let’s go back to this RA program data a little bit more. I’m going to show you an example of how some of this data just sinks into that sinking place, sunken place and talks about why you get stuck in this place because the data that you end up sharing ends up hiding behind numbers and you will not be able to tell a story in an intentional way.
So here, let’s take a look at this fall total example. For example, you see these numbers here, so let’s just blow this up a little tiny bit more so that we can see it a little bit better. So now you have fall totals between 373 all the way down to 798. And so that means if you total all those things up, it’s 10,833. You can also see this distribution that some buildings are doing better than others, but nonetheless, you’re kind of getting a snapshot quality of what it is our buildings are doing, how well our RAs are doing and this visualization in and of itself is not exactly unhelpful because you could share this information with your student staff and saying, “Look at the amazing work that you’re doing. Look at the 10,833 opportunities that you had with students around just an RA programming piece.” And thinking about what is the impact? 10,833 could be a small city, a small little town of … Millions of cities across the country, that’s how much of an impact that potentially the RAs are creating here at USC.
But nonetheless, you could have these conversations in a really empowered way, but it doesn’t really tell a whole story of what exactly it is, who’s attending, what are they learning, all those other kind of things. So as you start thinking about what is our data doing, you need to start assigning some value into what you’re collecting. Earlier, I shared some numbers around duty calls and all those other kind of things. And for example, duty calls, 5,892 calls that students felt unsafe. We now start to think about data as an opportunity to tell a story rather than just say, “Hey, how many duty calls did you assemble?” That is 5,892 times that you were able to respond to a student that was feeling unsafe and to be able to support that student in different ways. Program attendance earlier, that number was flashed 8,752. That’s another time that we’re talking about a student that’s seeking connections in some ways.
That student is coming to a program, not because of the fact that they want to attend a program or it’s because an RA is trying to cross it off of their list, but it is an opportunity because they want to make another friend. They want to make another opportunity to connect with somebody. Maybe they want to learn something else. Maybe they just want free food. Whatever it is, it is a student seeking some level of connections in some way, shape or another.
Sorry, let me take my little handy-dandy Roompact water here. Okay. Care reports, so 873 care reports, it is a student that’s needing extra support. What are we doing with that information? So when you start collecting some stories behind the data and numbers, you start gaining information that looks somewhat like this where it’s all over the place, and you start to think each individual dot represents, on this colorful map, becomes a point in which some level of story is hiding behind an interpretation that you still need to create.
So it is our opportunity to start thinking about this data as a way to organize it in a way, [inaudible 00:16:38] talks about what does it mean? How can we start combining some of the things that we’re talking about together and starting to merge them in as one story or one pathway into a conversation where you can start creating the tools to better equip yourselves to be able to talk about, what does it mean to create a residential experience that’s meaningful? What does it mean to create an ecosystem that is supporting a lot of this data and to talk about it?
So let’s move to our next chapter, which is the discovery phase. As you’re thinking about it, data visualization is not just about making data free, but it is making data easier to understand. So here’s a really, really crude example that I wanted to share here. Here’s a bunch of letters on the screen, you’re going to see them. And if I told you, can you just count the number of Bs that exist in this chart? It would be really hard, but you would probably eventually get to the, “Okay, there’s one B on the first row, there’s another one on the second row.” But if I presented the data in this way to say, “How many Bs are there in the chart?” I did the exact same thing, but changed the color on some things and now with the visualization, you’re able to see an assessment answer pretty quickly.
So that is what visualization is. It’s just literally data and how do we tell the data story a little bit better by making things highlighted and clearer so that people can understand a little bit better of what’s the story behind the story? The letter B doesn’t really have a great story, but this also highlights how you can take certain parts of this data, highlight it, and create a story in a way that visualizes a story that basically gets us to better understand how is our operations working, can things be more efficient? Can things be improved? Are we assessing the right thing? Are we able to tell a different story in a way that sort of changes the way that we can maybe potentially change our operations to make things a little bit more succinct and better for our students as well as our staff?
So let’s think about that again. Earlier I shared the example of the RA programming, and let’s bring that example back. And let’s go through a mechanism here to showcase how is it that we can do a couple things to highlight some things. For example, let’s look at the fall program totals. Again, these are the numbers, but let’s add the RA programming numbers to think about what does that mean?
So let’s bring those two numbers together here again. Again, the total was 10,833, but then the number of program totals was 578, so that means that we had an average program attendance of 18.74. You bring those numbers in together and you see that the number one building is over here on the halfway through the right side, and then two and three are over here on the side, and then four is down over here. I’m not putting any building numbers on it, but you can see the one, two, three, and four rankings here right here.
Let me pull in the last slide that I put up on the chart, on top … Actually, the average is 1,874. So here’s the average line that we have for the who’s performing above average and who’s performing under average. As you can see, six communities are above average and nine communities are under that average number. However, it’s an average. So of course somebody’s going to be above and somebody’s going to be under. No matter what, it’s a moving curve, so that’s what’s just going to happen. But you can tell who is performing better than the other communities when it comes to the average number per program. So when you start thinking about how are they advertising the programs, where is their program? What time is the program? How are they tracking attendance? How are they promoting the program? And is there a newsletter click rate or a RA that’s putting up signs at a time or Instagram posts or Snapchat messages, or whatever that may look like. The data that we’re collecting around why certain programs are becoming more successful could also be a really great research that you can make.
Also, here’s another thing. Could these buildings that have higher number attendants, could that mean that they’re lacking community connections and they’re seeking RA programs as a mechanism to connect with other people or not? Now you start asking some questions about the discovery about what are we trying to discover with certain data that were presented?
Let’s add another element to this graph. Earlier, I talked about the program attendance. If you can see the number one program attendance, when you average it out to the number of people that are coming, it’s actually number three when it comes to the average program attendance. If you just take the number of program attendees and just simply repeat that, the efficiency of the teams of bringing people, or maybe even, I’m not a huge person that harps on numbers that much, meaning I strongly believe that an RA that only has one student attending their programs is just as important as 20 people attending their programs.
However, as we start thinking about the steward of budgets are getting tighter and we’re thinking about what is the greater impact about how we’re making our dollars go a little bit further because budgets really need to be justified and valued around why we’re spending where we’re spending, these kind of conversations in saying the number of students attending programs for the dollars that we’re spending is far outweighing the conversations around what are they learning or all those other kind of things right now at this current state of this university.
So when you think about the number four right here on the right-hand side, they’re really having a higher average with a really, really, really small number of student attendee programs, meaning there are 17 programs and 409. So that means that they’re having a lot of students show up to their programs without having a great number of programs. Their problem is the number of program, if they were to increase the number of programs their attendance would immediately skyrocket because the fact that per program attendance is pretty high. So maybe start thinking about how are they advertising? What locations are they doing these programs? You start thinking about these visualizations as ways to prove what exactly is happening and how do we retell that story or recalibrate other stories to figure out how do we fit that mechanism?
We just talked about all these areas that we’re thinking about. And when you’re thinking about RA programming, I only use attendance at programming or the number of programs. However, all these things are connected to some of these locations. How did RAs learn about a program and how do they think about assessment? Once you start combining all these data points and figuring out a way to overlap certain answers, then you start thinking about how is it that you might think about RA training differently? How is it that you might think about supervision of RAs differently? How would you think about maybe crafting your newsletter messages differently if they’re not opening it at a particular place? How are they learning about certain things? You start digitizing and sort of thinking about data as entry points into students’ conversations, but also entry points into your way of thinking about your department’s efficiencies as well.
So start thinking about this from a perspective that data plus context plus the human experience becomes a story. And so if you start multiplying a lot of the scopes of what you’re talking about, what you do is get this beautiful equation that you will be able to replicate a lot of these efforts that you’re making, and it’s making it much more efficient in a way that consumes a little less energy all the time. Instead of always having a narrative that student staff are always tired or student staff are always not following instructions, or our RDs are always overwhelmed with a lot of things, you can start to think about what are they overwhelmed with, why are they tired? What is going on with our department in some ways and creating these deficiencies? And I only used RA program as one mechanism to do that. There are so many ways to do this, and I’ll go over some examples of how we can move forward with this process.
So chapter four, let’s climb up a little bit higher on this. So sorry.
Amanda Knerr:
We have one question.
Shigeo Iwamiya:
Yep, I saw that and I got distracted, unfortunately.
Amanda Knerr:
You may get to this, but where do we get the time to fit this in or how do we build it into our routines where it’s not more work but just a part of what we do?
Shigeo Iwamiya:
Yeah, that’s a great question. And thank you, Paul, for that question. Later on, I do talk about that we need to create a culture of assessment and create a space to be able to do that, but it’s not easy. So I recently started Duolingo and I am in a hundred-day streak or so, and it is taking a little bit of time for me to get to that place of level five or level six or level seven of the language that I’m learning. Assessment or culture of data review is also another language. It’s a data visualization language that you need to learn how to speak. If you think that you need to be able to learn that and create and go culture around that, you have to make time and figure out a way to do that and change the way that you’re working on some things because it’s not going to come overnight.
I used to teach lessons of tennis and our younger new players would always just try to say, “Well, I’m just going to be like Andre Agassi and I’m just going to swing the racket as hard as I can and possibly learn, and I’m going to make those big shots right away.” And everything that you do, it’s a skill, it’s a learning, it’s like supervision or anything like that. You have to take it in small bites and be able to learn that maybe what I’m looking for is not immediately identifiable and you need to start training your eyes.
I’ll get to that in a little second. So Paul, I’ll come back to that question a little bit more too. But at the same time, I want to make sure that I leave space to be able to say, this isn’t immediate, this is not easy, and you have to set aside to take time to learn that. And it’s also on me, people like me that are directors or executive directors around the country to say, “Hey, staff, let’s think about engaging in this conversation so we can lean into that and create a culture around assessment and creating a culture that that is normal or expected or part of the performance evaluation and understanding that it’s incorporated into the world where we can create space for that to be okay with.”
So as we talk about the climb, I want to make sure that we talk about how we’re going to build on this experience and grow into it. So one of the things that we need to think about is what is your model and what’s working for you? For example, we have schools that are in the curriculum models, schools that are in the programming model. At USC, we used a little bit of a hybrid. We have intentional conversations, but we have programs and we don’t necessarily use a curricular model to approach certain things. We’re all over the place when it comes to taking bits and pieces of the best parts of everything and try to incorporating into a lot of the things that we do. So what model works for you in figuring out what you can assess and what programs that you’re using for assessment is by far one of the most powerful things that you can think of. Some people use Excel, some people use a platform that were already created, but whatever you use, as long as it works for you, that’s the most important thing.
So whatever the engagement is, you then have a data source that you collect information on. That could be Roompact, that could be an Excel document, it could be a database that you have, you could just be using ID swipes or Maxient or Advocate or any one of those programs that are tracking information. You might have StarRez that’s tracking information, whatever that may look like, that’s important for you to understand what your data source is and how you’re collecting that information. And then you process the data and think about where is it going, it’s creating a chart in some ways to understand, is it an Excel download, whatever you’re getting to talk about that.
And then you start looking at an analysis and thinking about, well, what does that data mean? There’s something there. This is the day when I used to think about back in my grad school days when I would dump Excel charts into a SPSS or a mini tab and think about what are the P values coming out on this one and say P equals 0.006 or 3 meant that it was somewhat significant. So at the same time, I’m looking for those values, looking for some correlations to thinking about, well, could this be something?
And then we create a story that goes along with it and then thinking about, can I create something? And then if so, can I visualize it and create a example that’s easier for people to understand because just presenting data might not work, but creating a data narrative or a visual narrative, now that is becoming a little bit more powerful and it becomes a marketing strategy for your part to be able to tell that story.
And then you think about operationalizing it. How do we change that information and share that with the team? How do we think about who needs to know information? Who needs to be advocating on your behalf? And so thinking about how do we operationalize all the important things that we just talked about and then thinking about what changes we might want to make.
So that becomes your new ecosystem for your data in some way, shape or another, because it’s creating these cycles to make things work. But before the engagement, there should have been things like what’s your model? Why are you collecting engagement? If you have a programming model, if you don’t have a programming model, but all you’re doing is programming, what does that really mean? So you’re collecting data that doesn’t even make any sense. But because you have a model based on usually a learning outcome, students will be able to blank, and that’s why you build your models around certain things.
So you create these data ecosystem as to why you’re doing what you’re doing is mattering. And then you create these cycles that really, really combine a lot of the things that we’re just talking about, and then turning them through a system to repeat itself over and over and over. And then hopefully once you operationalize some things, you create into learning outcomes and reevaluating our people learning from those learning outcomes. Like if we were going to say … Here’s an example, if you say one of your learning outcomes is, students will feel safe on campus and you want to know if students really truly did feel safe on campus or creating opportunities for students to feel safe on campus, we want to be able to assess some of these things.
And let me walk you through the process of maybe even data visualizing this and see if we can make this work. So here’s that ecosystem again. So let’s think about this process and work it through and see what happens. Let’s think about this. So students on campus being safe, the first thing that comes to my mind, and this is somewhere that I would start, is look at the data from how many calls are coming into our RAs. I’ll use that as an example, as a starting point because earlier the question was, how do you create time to do that? Well, you have to find a starting point and chew off little tiny pieces at a time to thinking about how did that impact things? What questions did that come up with? How many more answers can I come up with?
So let’s just look at the RA calls for the fall. Here we have anywhere from 464 at the top to 307 on the bottom here. And so let’s map these out. I look at these out and think first period is the light blue, the middle period is the middle blue, and the third period is the dark blue. If you can see, I can see that the first period is higher than the other two. So that’s my first gut instinct here, is thinking, “Oh, okay, cool.” I couldn’t see that in the numbers in the chart, but now that I put it in a graph, I can start to see, okay, the first numbers are a little bit probably higher. I’m just visually looking at it and thinking, “Yeah, that’s it.”
So let me just try to see if I can create something here. So period one, total it up is 1,636 calls total. Okay. The second call is 752. The second period is 752. I can see that in the visual chart that it’s going down too a little bit, so that makes sense to me. That is a decrease in 54%. I’m doing numbers behind the scenes, but I’m also putting it out there in a little red little circle thing here that shows that, hey, on period two calls are going down. Period three is slightly higher, 964. I can visually see that the period three is going up as well. So that’s an increase of 28% from the previous period. So that means it starts off high, dips low, and comes back up.
You can’t see that from an Excel chart very well unless you have a really keen eye that you can sort of say, “Oh yeah, yeah, yah, I’m looking at this and I can skim through the numbers and now I can clearly tell that the first period is the highest, third period is second highest.” So you could create that. And it’s a little bit more than two-dimensional. Now you’re starting to see ratios and comparisons behind certain things.
But then it dawns on me that we have 16 weeks in a semester and there’s three periods. So if you average that out, at the very least, it’s six, five, and five. So there’s a shorter period there somewhere. I look at it closely, it’s six weeks, four weeks, and six weeks. That’s what the breakdown is. So wait, the period two is only four weeks, but it’s also the lowest period. Is it because there’s two less weeks that’s a little bit lower?
So I think to myself, let me look at another number here to try to see if I can compare a couple things. So I take a look at the average number of calls per day. And then you start looking at it and think, “Okay, the numbers, again, don’t do anything for me, but at the same time, let me just try to see if I can visualize this in another data.” So I then put it out in another data here, and then I start to see the same thing here, high in the very, very beginning, but this time, instead of a peak coming back up, it actually looks like it’s coming back down on both sides. So I think about RA average calls per day, and I think period one, 2.6 calls per day per area. So each building is getting about 2.6 calls every day. In the second period, there’s about 1.8 calls per day, and that is a decrease in 30%. And then I think about the period three per day, and now we’re talking about another 30% decrease.
So initially when I was thinking calls were actually high and then got really low and then came back up, that data, that information story behind the story did not appear to be a correct story that I was going to tell because I wasn’t looking at all the combinations of data. Again, earlier I talked about visualization is not just making data pretty, but telling the story that really matters and how do we implement some of these things.
Now, if you told RAs in your first six weeks, “You’re going to receive anywhere between two to three calls per night, but in the next 10 weeks after that, it’s going to start dropping down slowly.” So that most effort that you’re going to be making around duty in the early parts are making the connection to the early parts, intentional conversations, programs, those kind of things are extraordinarily important because that’s going to have an impact on your duty calls because early on, these are some things that really, if you work hard, you can sort of ease up a little bit later. Maybe your staffing structures start to change. I’ll get to that in a second, but just looking at the average calls per day just suddenly changes that conversation about what the duty calls really did.
So here’s that learning outcome again. Here’s that chart that we just created, average calls per day is that you start off at 2.6, but it slowly starts dropping down 30% at a time by every assessment period that you go. And then you start thinking about RA training, are we communicating with this to our RAs saying, “Hey, this is the structure.” Anticipate impacts. This is a little bit of a crude example, but when we do active shooter training, it’s all in the preparation, like know where your exits are, know where you might run to.
If you talk about this at RA training saying, “Hey, this is the busiest time of the … ” I mean, there’s going to be one-offs and you’re going to obviously have some ups and downs throughout the semester. However, over average, the most highest period of calls are going to happen in the first six weeks. We all anecdotally know this, but if we can visually represent it in a way that RAs can understand walking away from training and learning that, “Hey, if I work really hard on the first six weeks, the rest of it starts to get a little bit easier. Maybe this will start contributing towards my burnout rather than feeling like in my first six weeks I just burnt out, in the next 10 weeks, I just don’t know how to respond anymore.”
We see this all the time around college campuses that the first six weeks makes or breaks an RA’s experience. And so now you’re talking about, is this information communicated in the RA training assessment? Can we show that this was information that was retained or not? And then also thinking about RA duty hours, is this coverage adequate? What if you put three RAs on duty from the first two weeks and then the next 10 weeks, you only had one. Do you build a schedule in reference to what you’re seeing when it comes to the number of calls?
So let’s think about that. If you’re talking about this as a one singular fall 2025, but suddenly you do this over and over and over in Fall 2024, Fall ’23, ’22, and you start putting all four of those together, or however many you say you have together, and you start to see, wait a minute, on period two, on fall ’25 to ’22, you’re starting to see anywhere between 31% drum to 26% down and period three, pretty much 26 to 30%, but it is very consistent.
Now your story is becoming powerful, is because now you’re starting to see years and years of data compiled on top of each other and stories telling stories on top of stories. Now you’re starting to see that not only just one story is true, four stories is true. Now you have something that you can take to somebody and say, “Hey, listen, I really do want to change the structure of how RAs are on duty, or I think we need to train our RAs a little bit differently because of the fact that now we’re starting to see a consistent way to evaluate and also make our operations far more efficient.” I’m just doing this using duty calls. I’m not even using incident reports filed, care reports filed, or how many roommate conflicts that they’re responding to or anything like that. When it comes to students feeling safe on campus, I’m only using one data point or maybe two if you want to use the calls per days, but I’m taking a dataset and converting them into visuals to powerfully tell us exactly where and how we can respond to certain things.
And so now you’re operationalizing, like what we were talking about, you went from visual to now operationalized and say, do we need to reevaluate how we do this? One thing that I’ve always known, and I’ve been working in the field for 25 years, if it’s a structure that’s working, two RAs on duty, anywhere from Thursday to Sunday and Monday, Tuesday, Wednesday, it’s one RA. No questions asked. That’s just what we do. But why? Why is it different? Why is it not different the first six weeks? We do a really good job of putting extra people on duty for Halloween or those kind of things, but that’s because we know Halloween is busy. If we know certain days are busy and certain days are not, maybe home football games, whatever it may be, but now we have data to prove it. And not only do we have data to prove it, we have a story behind the visuals that we can use to prove what we’re doing.
And that’s just with students feeling on campus and one learning outcome that’s using all these mechanisms, all these mechanisms, I just told you that I used duty calls and only duty calls, but here are all the points that you could have used to think about how students are feeling safe on campus. So that’s just how … You could have used care reports, how many care reports, how many care reports per day, per week. You could have used roommate conflicts per day, per week. How many are resolved? And then also you could have used medical transports. What kind of transports are happening? What times do they happen? Any specific kind of communities that are impacted harder than the others?
All these things are true when it comes to that singular learning outcome that I could have taken any pathway to figure out, but at the same time, you could have created a visual to demonstrate exactly what was happening. You could have overlaid it with a campus map, whatever that looks like, but you could have created something to create a story behind that to better understand what is going on. That’s just with one learning outcome. There are multiple learning outcomes usually. And so now you start thinking about the level of how you can tell these stories in a powerful way and how you might be able to design certain things to make it happen.
Let’s move to the next chapter here. Let’s think about the rise of how do we think about … Then now that we have this data, what do we do? And now that we have the story, now that we have this opportunity to think about what is it that we just did and think about what we did, what we do. Here’s the ecosystem and then here is all the points in which I said that I would collect data, let’s think about all the places that you might be collecting data in. You might be collecting data in Roompact, you might be using Power BI, you might be taking Tableau, you might be taking Excel charts, you can take any of the creative suites around Apple products. Anything that you’re using, all those locations are places that you want to store data and create data and publish data and tell your story because those are powerful experiences to be able to create that.
One thing that we’ve done at USC, and this is where we’re going to … I’ll show some things. We’ve created a residential impact dashboard. This is a printed report that we create every single assessment cycle. And if you can see, this is period one, a lovely picture in the front with our students, but we’ve created these dashboards that think about, and we print these and share them with our RAs to think about what are they doing? How many people are attending your programs? How many people are coming? Not because the snapshots are information that’s important. Earlier I talked about, we need to go a little bit deeper, but when it comes to the reflecting the experiences of what are the RAs doing and how is it contributing to the department, we can share these things. We can talk about how much money we’re spending on the programming, what is it costing per program or per attendee?
And we can start getting into this place where we’re talking about how our intentional conversations, which we call Trojan Talks, what is the completion rate? What are the conversations that are happening? How many conversations are happening? When it comes to Trojan Talks follow-up surveys, we asked a question such as, “My RA has been supportive of my USC experience.” Now we’re starting to put some color into the conversation about not just the Trojan talks of how many and how long it’s taking for them to have the conversation, but what are the students reflecting on that experience and talking about that. We talk about supervision, advising, RA training, bulletin boards, and then we also talk about emergency response and care and res life, late night projects and all sorts of things that we work on. I’m so sorry, I saw a question come in, so I’m going to make space for that. If somebody could read that question to me, that would be great. Or a comment came in.
Amanda Knerr:
No question, just a smiley face with hearts.
Shigeo Iwamiya:
Very cool. I’m so sorry. I can’t see the live chat coming in because my screen has been taking over our screen share. So all that to say that these documents become super important to be able to showcase the efficiency or your health of your organization in some ways to say, how well are we doing? So these ecosystems that we create are far more important, but like earlier we said we have to create a culture of assessment. If you don’t, the data will still live in a sunken place. It will be there. But in order for you to say, I got to do something with this, we need to create it within our practice. We need to say, I’m going to take an hour out of my day to look at this data to think about, okay, what kind of story would I tell? What would my supervisor want to know? That’s always the kind of place that I really started is what would my supervisor want to know and how can I advocate for my team a little bit better?
And so I think about this, and I’ve always thought about this my entire career in some ways about how do I create a culture of assessment? The person with the best data wins is something that we always hear in student life, and I want to be able to talk about this culture of assessment that you create or you seek others to create in many ways to help creating a community.
RAs can help with this. They’re collecting data all the time. And maybe you can have these conversations about what do you want to see? How do you want your RA experience to be reflected? What would be helpful for you to be motivated at your job? Because a lot of times we talk about these positions being super thankless. Well, does it have to be? That’s my biggest question is we always talk about student affairs as such a lonely place and you don’t get paid enough to do what we do. Well, let’s talk about that. Let’s compensate people with different ways in which their recognition or their work can be really reflective because nothing is more important than us feeling like we’re important. Then we visualize some of these stories and kind of create a narrative that works for us in some ways in a powerful way. And then also with that narrative, how do we make sure that leadership decisions are being made based on the decisions or the demonstration or the visual information that we provide?
And very lastly, I wanted to talk conclusion-wise, we talk about these data points that is extraordinarily important. They’re scattered images, but each one of these points has a story behind them. It’s a person that needed help. It’s a person that needed a connection, the person that wanted to step up and become a leader, a person that wanted to be heard. Maybe it’s a person that met their best friend because they had one of the biggest experiences at their RA program, or maybe it was an RA that met another RA that became life partners. All those stories are right behind those little tiny dots. It is our job to organize it in a way that sort of talks about what happened. Data is what tells us what happened.
And these dots really talk to us about opportunities in which all these things happen, but visualization starts to show us some patterns so that we can identify what’s working, what kind of trends are we seeing, are there opportunities to change? Are there ways to make things efficient? If patterns are negative, we have an opportunity to lean into it and say, let’s fix some things or let’s at least talk about it to figure out a way to make sure that our trends can either be stopped or increased depending on if they’re good trends, we need to figure out a way to make that better.
And then the storytelling helps us understand why it matters. It goes right back to that learning outcome that we talk about. We say that our students want to be safe. We want our students to be safe. Can they get a sense of safety? Can they get a sense of them feeling safe? If we do our job correctly and take the time to assess it and say, “Yes, we can make improvements based on X, Y, and Z, and we can deliver on the learning outcome because of the fact that we’re actually making things efficient,” that’s what matters. And so it’s not just about data, it’s not just about visualization, but when you combine both of those together and start creating stories, it really does change what we do and why it matters.
And so with that, I wanted to conclude in saying thank you so much. I really appreciate the time that you were able to spend with me and I wanted to open our conversation up for any questions. Hopefully you enjoyed spending time this afternoon with me.
Amanda Knerr:
Fantastic presentation. Are there any questions as we’re wrapping up for today? All right, we’re getting some positive, “Yay. Thank you. Great job,” in the chat. Shigeo, thank you so much for being our featured speaker today. What a wonderful presentation to help us think more clearly about strengthening not only our assessment efforts, but our data-informed storytelling. So you can use the assessment data to really promote action and decision-making and advocacy for our students and our teams. We really appreciate your time and your insights today. I also want to thank all of you for participating with us today. We’re glad you were here and able to be part of the conversation today. You’re going to be receiving a follow-up email in the next few days with access to today’s webinar recording, and if you’d like to revisit the session again or share it with a colleague or a friend.
Before we go, we’d appreciate if you take just a few more minutes to complete a brief feedback survey. Your input helps us to continue to improve these sessions. I’m putting the link right here in the chat now, and we would love to have you fill that out if you have a couple of moments. We also hope that you’ll consider joining us for some upcoming webinars that we have. The next one is Duty and Dragons: A Game-Based Approach to the Incident Response Training on Friday, April 10th at 2:00 Eastern Time, and Operating as a Learning Organization, which will be on Wednesday, April 29th at 3:00 Eastern Standard Time. I want to thank you again for your time today, and I hope to see you in a future session. Have a wonderful rest of your day and a great weekend. Thank you.
