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The Grand Challenges Data Visualization Committee is collecting Case Studies and Success Stories about how institutions have used data visualization and/or storytelling to help drive decision-making, innovation, and positive change.
If you have a case study or success story you would like to share, please contact Grand Challenges - Data Visualization Team Co-Chairs, Becky Croxton (becky.croxton@colostate.edu) or Kelly McCarthy (kemccart@usf.edu) for more information.
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More studies coming soon!
Florida State University
Florida State University
Institutional Performance and Assessment
Caitlyn Jessee, Assessment Analyst | Email: cjessee@fsu.edu
General Education Student Learning Outcomes (SLOs) Report
General Education Student Learning Outcomes (SLOs) Report
The General Education (Gen Ed) Student Learning Outcomes (SLOs) report is an interactive dashboard designed to support academic departments and faculty participating in assessment and reporting of Gen Ed outcomes. The report effectively visualizes levels of learning demonstrated by FSU students on the standardized set of SLOs in sampled courses. Please note that this specific, externally facing example was built using contrived student data for a fictitious department of Agriculture.
The user-friendly dashboard integrates data from FSU’s learning management system, student information system, and various internally managed files. The report presents SLO assessment data in such a way that it allows for optimal student learning analysis. Users can progressively navigate the report revealing learning data at different levels of granularity – from academic department to specific Gen Ed course(s) and course section(s).
The dashboard is accompanied by a fillable template that ask instructors to provide answers to several questions about student learning demonstrated for each SLO in different courses and sections, and about changes that will be implemented to address any gaps or insufficiencies in student learning. With both the interactive report and the template open, users can document their findings as they navigate the dashboard. This centralized approach to data collection and processing allows instructors to shift their focus from data wrangling to data use. This approach has significantly contributed to the continual enhancement of assessment practices and culture at FSU.
FSU Office of Institutional Performance & Assessment:
Institutional Effectiveness (IE) Assessment Submission Status Report
Institutional Effectiveness (IE) Assessment Submission Status Report
The Institutional Effectiveness (IE) Assessment Submission Status report is an interactive dashboard that shows the number and percentage of Student Learning Outcomes (SLOs) and Program Outcomes (POs) for which FSU academic and non-academic units submitted assessment plans and results in the University’s system called the IE Portal. This report allows users to view reporting progress at each level of organizational hierarchy in a ‘drill-down’ format. In addition, data can be viewed by specific years and for specific summary units: Colleges/Divisions and Departments/Offices.
This relatively simple yet robust tool is used daily by staff in the FSU Office of Institutional Performance and Assessment (IPA) and by the larger campus assessment community. The IPA Office uses the report to monitor university-wide assessment activity levels and identify specific campus units in need of a reminder, offer of support, etc.
The report is especially useful to (Associate) Deans, Chairs, and Directors involved in reviewing and approving assessment reports submitted by their programs. One notable case is the College of Arts and Sciences, which includes 16 departments, nearly 100 degrees and certificates, and over 400 outcomes. The Associate Dean of this College regularly references the Submission Status report to determine which departments and programs’ assessment reports are ready for review and which units need to be contacted to ensure timely submission. The report addresses the previously challenging task of tracking timeliness and completion and helps shift focus towards providing insightful feedback to reporting units.
University of North Carolina at Charlotte
University of North Carolina at Charlotte
J. Murrey Atkins Library | Phone: 704-687-0494
Library Instruction by 4-Year Graduation Rates
Library Instruction by 4-Year Graduation Rates
At UNC Charlotte's J. Murrey Atkins Library, a data visualization created using Tableau helped reveal a gap in reaching our transfer students through library instruction. Transfer students make up more than 30% of our campus population. Since 2018, the library at UNC Charlotte has led a a multi-year institutional-wide study in which we gather, align, and continue to analyze 10+ years of undergraduate student engagement data. This data set, aligned at the individual student level , also includes demographic information and broad-scale measures of student success (year 1 to year 2 retention, graduation rates, cumulative GPA).
In 2021, the study team conducted a study focused on transfer students, how they engage with co-curricular and extracurricular services and activities on campus and the relationship these experiences have on student success. Through the insights we gained through the visualizations, the library made a successful case to the provost for creating a new library faculty position focused on student outreach and success. In the past two years since this role was filled, we've already seen success in broadening our reach to our important transfer student population.
Illinois State University
Illinois State University
Ryan Smith, Director of University Assessment | Email: rlsmith@ilstu.edu | Link to Report
Data visualizations face multiple demands in academic settings. Some users want quick, story-driven insights. Others prefer the details, including metrics and statistics. Satisfying everyone’s needs can lead to cluttered and overwhelming designs that serve no one. This also increases the workload for assessment professionals as they build multiple visualizations with little impact.
To address these competing needs, I used Power BI’s tab structure to create two dashboards: one story-based and one data-based. The story-based dashboard is explanatory – it uses data to walk users through narratives. The data-based dashboard is exploratory. It invites users to explore the data and create their insights. While both dashboards incorporate elements of storytelling, they differ in approach.
Story Dashboard
Story Dashboard
The Story tab uses dynamic text and a minimalist graphic to highlight stories and insights for each NSSE category.
• Dynamic text in the visualization title and implications box change based on two user-selected filters: NSSE category and student class level (first-year or senior). This is done by writing a short 1-2 sentence story for each visualization. A column is added in the Power BI table and linked to the filters using a simple query.
• The visualization itself is a minimalist vertical dot plot. The large red dot represents Illinois State University and is consistent with university brand. Peer group averages are represented by a dark grey dash. The visual distance between the two elements supports the story in the visualization title. Even novice data users can quickly grasp the visualization’s insights.
Data dashboard
Data dashboard
The Data tab is designed for deeper exploration and includes a table with NSSE questions, scales, scale means, effect sizes, and p-values. To aid interpretation, p-values are color-coded in three tiers: <.05, .051–.099, and >.10. Effect sizes are visualized using positive or negative horizontal bars. The bars show the direction and strength of differences between ISU and its peer group.
While this tab emphasizes , it also maintains visual consistency with the Story tab,
There are also columns for effect sizes and p-values. P-values are color coded in three tiers: <.05, .051 - .099, and >.10. In order to see the direction and strength of the difference between ISU and peer group scores, an effect size column uses positive and negative horizontal bars. Users can filter the data by class and NSSE category. The table can be sorted in ascending and descending order to view the most significant differences or largest effect sizes.
Summary
Summary
Although the dashboard is split across two tabs, the user experience is generally the same. This is accomplished through consistent filters and layout, a shared theme of comparing ISU to peer institutions, and a navigation button at the bottom of both dashboards allowing users to toggle between views.
This project reminds us that a dashboard doesn’t have to choose between storytelling and statistical. Using the tab function in Power BI, a “one stop shop” experience can be created that accommodates a wide variety of users.
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