Three D’s of Data-Driven Course Design

Concurrent Session 4
Streamed Session

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Session Materials

Brief Abstract

In this presentation, participants and presenters will discuss and explore in groups, then as a whole, the Three D’s of data-driven course design: 1) what data they have or need, 2) what they do with that data, and 3) what measurable difference the data make in improving online course design.

Extended Abstract

Session Outcomes:

  • Share mid-course and end of course evaluation results
  • Built a community of Triple D users
    • Sharing data sources and uses in LinkedIn
  • Pre and post data – semester before and assess afterwards
    • Mid-course and final course data.

Topic and Relevance

The online educational community has been designing online courses for years.  How do we know if we are creating high quality courses?  Are we hitting the mark or missing the mark? Collecting, analyzing, and using course and student data to make improvements is one way to get closer to that high quality standard.  Zhongzhou Chen, UCF assistant professor recently said, “If you properly design an online course and analyze the data, you get very detailed information on what is working and what is not working for the students…That is how online learning resources can be continuously improved.”

In this presentation, we will share the type of data we are collecting (LMS and student surveys), how we use it with faculty on a weekly basis during pilot semesters, and how we use it as a mid-course and end-of-semester meeting with faculty to make continuous course improvements. We will discuss how the presentation of the data makes a significant difference in gaining faculty buy-in. We have found that they become eager to make the changes that will improve their courses.  We will also show pre and post data as a way to measure the difference the data-driven improvements are making (or not) in the student experience.  We will show how this data-driven improvement process has worked in a couple of our courses. 

Plan for Interactivity

We plan to have participants work in small groups at three different times during the presentation to explore what data they use, how they analyze it, and finally, how they measure the effectiveness of course modifications. To accomplish this, we will establish a collaborative environment of learning from and teaching one another, in smaller groups and then as a whole, during our time together.  


  1. Participants will leave as members of the LinkedIn OLC 2019 Data Group as a community where we can share slides, surveys, dashboards, graphs, and data that we produce and use in our programs.  We can continue the conversation there as we have future questions and contributions of how to use our data in ways that make a measurable difference.
  2. Participants will leave with a clearer vision of what data they have (or need), what to do with it, and what measurable difference it can make.
  3. Participants will leave with examples and ideas on how to use data on a weekly basis during a pilot of a new course, and how to use mid and end of course survey results and course data to make iterative course improvements each semester.
  4. They will also leave with examples and ideas on how to compare pre and post data in courses to see the difference those changes make.