“Once Upon a Time, There Was a Huge Data Set”: Telling Our Unique User and Usage Stories with Data
Concurrent Session 10
What is it you need to know from your data? We’ll explore strategies for honing your data needs, working with vendors and others to get the needed data, and how to coalesce it all into the story you need and want to tell.
Every year the technology landscape changes, at a seemingly exponential rate of increase (a lá Kurzweil’s Law of Accelerating Returns), especially after the global changes in the use of educational technology following the COVID-19 pandemic. Institutions grow and adjust to suit the changing needs of their communities. In many cases, data is available and can be our best way to see how the changes are affecting our communities and how we can best leverage the tools and initiatives for the betterment of our institutions’ communities.
In the last year, the Center for Excellence in Teaching, Learning, and Online Education (CETLOE) at Georgia State University has embarked on an ambitious quest to learn what we can about our Educational Technology toolset. We wanted to know who is using our learning tools, how much usage our tools have been getting, how much support is required for these tools, whether the GSU community is getting the support they need to make the best use of tools, and how satisfied the community has been with the toolset we have provided. We’ve started pulling raw data, and this presentation is grounded on what we've learned on this data journey.
It’s important to note that we are instructional technologists and learning tools experts, not data analysts, but with the ever-growing emphasis on data-driven decision-making in education, and the seemingly exponentially expanding sources of data, knowing how to refine our data needs and interpret data successfully has become a critical skill to develop. That said, raw data can be, well, daunting. So how do you know that the data you have is going to tell the story that you need to share about your learning tools, your initiatives, and your community?
It’s important to ask the right questions and to decide the goal for crafting your data story, even before digging into your data. Do you need to know usage trends and who is using your tools? Are you investigating a story to support further funding of a tool or decision to sunset it? Will you be trying to determine how users are engaging with the tool and where they may need support? These are but some of the many questions you may need answers to regarding tools and initiatives at your institutions and in your teams. We'll interactively explore strategies to frame your “data story” and identify the questions that will determine your specific data needs.
Interaction One: In this activity we will turn to our audience to generate and explore questions people have needed answered about learning tools and initiatives. Questions your colleagues share may inspire questions that could be helpful to your teams and guide you to consider your own stories in new ways.
Likewise, it is essential to remember that the data we have can often seem overwhelming, and that not all data is needed for all teams or team members. Some people are looking for straight usage data, some trends, data interrelatedness, and any number of other things. However, without specifying to a department or vendor precisely what you need, you simply get what ‘they’ consider to be a standard data pull.
Not surprisingly, a vendor or other third-party’s data pull is often organized to present the tool or initiative in the best light for their data story. It can be easy to get lost in another’s data story, making you work even harder to ensure that you are getting the data you need for the overall story and direction of your team. We’ll look at strategies for how to talk with your vendor to work with them in clarifying what the data is recording, how the numbers are determined, and more, to help increase understanding of the data you have and how it fits into your goals.
Interaction Two: At this point in the session, attendees will be placed into groups and a sample data set will be distributed to explore and evaluate based on a question developed in the first interactive activity. Once the groups have time to talk through what the data is or is not showing, we’ll return to the whole group to compare highlights from what was learned by reviewing the data.
Now that you have crafted your data story and are confident that your data analysis supports your story, you need to help others understand the story that the data tells. We will review ways to aid the visualization of the data story, as well as illustrate how not all graphs are created equal.
This session will explore strategies for explaining what is and is not present in the data, and how the nature of the raw data affects the story being presented. At the conclusion of this session, the presenters hope that all attendees are empowered and prepared to become better data storytellers, to have strategies they can take to their home institutions, and to become better informed advocates for the tools and initiatives they support.