1. Addressing Shortages in Classroom Space
Using Tableau To Develop a Model of Classroom Utilization – by Govind Acharya, Principal Analyst,
Our IR office is called BIA and includes the University’s budget office and Business Intelligence unit. We act as an objective source of not only data and analysis but increasingly of recommendations. This study was very much in the latter camp, with senior leaders asking for recommendations based on the data. This talk will provide an overview of what we did. I think that the lessons can extend to other more traditional parts of IR. But it’s especially helpful for something like classrooms where it’s not often clear who the “experts in the room” are.
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2. Takeaways From Today’s Presentation
IR’s Role in Classroom Utilization
Leveraging the Data
Visualizing the Results
Communicating Recommendations From the Data
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3. BIA’s Role in Classroom Utilization
Provided analytics by developing a unique data set.
Assemble a series of visualizations depending on the audience.
Navigate the multiple stakeholders desires.
Made recommendations on future needs.
Be willing to change course on direction of the project.
Classroom scheduling and use patterns is a complex dance between multiple stakeholders, each of whom have a very large stake in the outcome. Registrar, Facilities, Academic Affairs and faculty, Campus Planning, and Student Affairs. This sometimes results in competing recommendations, not always based on data. For example, comments like “our classrooms are full” or “the only times available for the class is late in the evening” could be true but was not backed up by data but anecdotes. The plurality of anecdotes, it turns out, was more nuanced. It was essential to have a objective source of analysis that could provide analytical heft but also data-driven recommendations. In the end, our office provided the recommendation that additional large lecture hall was the key to alleviating any remaining
IR’s role is unique on campus. We can provide objective data-driven analytics and couple that with recommendations. We have limitations though. We cannot provide recommendations based on non-data driven factors. For example, suppose a faculty member always teachers his or her course at 10am MWF in a room that fits 200 students. But the enrollment in the course is only 96. The data tell us that this course should be moved to a different room. However, the professor says no. We cannot incorporate those into our calculations. Nor should we. But as an IR office, we don’t have a direct stake in scheduling, or constructing buildings or teaching in the rooms. So we can present the data with context but as a more or less disinterested party.
But we also play a significant role because of what economists call the collective action problem. There are so many units working on disparate issues that one office needed to be in the lead. And it was determined that BIA was going to be it.

4. What’s the Problem That We Need To Address?
The problem we needed to address evolved and continues to evolve based on the dashboards and reports that we provide. The fundamental one that we address is this: is there enough classroom space? A secondary problem that occupied a major portion of the analysis was whether the pressure on large classrooms resulted in more courses being scheduled in the evening instead of a traditional 8am to 6pm period. But the questions and problems that arose became more refined as the months went on because FINALLY, people were seeing the data in a way that people understood.

5. What Data Do You Need?
Every classroom with the type of room, seats, which department controls it, and any other information you can grab.
Coursework with enrollment, schedule, whether it’s a lecture or discussion section or lab, who teaches the section (faculty or instructor).
When the course takes place: days of the week and time, the scheduling context over the years.
The sources of data is a really tough nut to crack. You have to think carefully of the fields you will need ahead of time. Not doing this means that you may have to roll this in after the fact. It’s a lot harder to merge in an important field after the fact. Trust me I know! I have been working in BIA for about a year and a half now but at the time of the project I was only on staff for 2 months when I started this project. So there were a lot of fits and starts with the data. In the end, there are 18 different data sources populating this dashboard. The key though is making it seem like it’s just one simple data extract. But some of the essentials are listed here.

6. 4. How do we illustrate the issues
Capacity Utilization: How full a room is for a particular class
Time Utilization: How often a room is used
Define the time period: daytime class starts between 8am to 6pm
The next thing is putting it together in a visualization. Because this was the first of its kind on campus, this also evolved a lot over time and depends on the question we need to answer and the audience. The solution was to design different visualizations depending on who needs to see it. It’s great for the audience but it’s very time consuming for the analyst. Luckily Tableau can do it really well.

7. WHAT ABOUT THEM? What about benchmarks?
OK– it’s not that bad. But if you remember a few minutes ago I said that BIA was asked to provide recommendations. Well, part of the challenge was that we needed to create the benchmarks, metrics, and visualizations, get feedback, and refine them. In the end, we developed a number of benchmarks that addresses the needs of our stakeholders. As we cycle through the visualizations I’ll highlight them in more detail. Note that these visualizations do not have much information because they are contextualized in static documents due to issues with how we deliver the analytics.
The first is going to be a more service oriented dashboard. These are not available to public so it’s difficult to share. I’ve been using PDFs to share them across campus. There isn’t a particular reason why it shouldn’t be public but we haven’t pulled the trigger on a decision yet.
A few to talk about as they come up:
low enrollment given the room it’s in.
Capacity utilization > .85
Time utilization > .85
Day – Evening metrics
Classrooms that are either under construction or about to be built.
Future enrollment patterns.

8. Challenges and Opportunities
Distribution/audience
Interpretation
Refreshability & Lag
Opportunities
Collaboration
Extensibility
Because of our use of Tableau Public and Desktop, it will be very time consuming to ensure that the right audience is seeing the right visualizations.
Interpretation requires a lot of context. As we become more expert with the data and scheduling metrics, we may be able to improve our visualizations to be more explanatory.
The main data source for the dashboards do not become available until several weeks past the end of the quarter. Plus the process of appending the quarter’s data to the existing dataset requires a fair amount of work.

9. You can also contact me at gacharya@ucdavis.edu
Questions?
You can also contact me at
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