Working with a small social enterprise, I designed this online course to teach international development and monitoring and evaluation professionals from all backgrounds how to create both static and dynamic data visualizations in Excel. My design process began with identifying in-demand Excel skills and how this course could differentiate itself from other courses available online. After identifying the ability to perform basic data cleaning, complete key formulas such as index-match and vlookup, and create interactive visualizations using PivotTables and slicers as learning outcomes, I decided upon brevity and interactivity as the missing components of other courses on the market.
To generate bite-sized lessons, I created standalone screencasts on data cleaning, formulas, and interactive visualizations that covered a single topic within a few minutes. To encourage interactivity, I built in animated presentations and case studies. I also created two custom scenarios in which the learner chooses the appropriate Excel function or visualization technique for a given set of criteria. By actively adjusting elements of the chart and learning from mistakes made, the learner demonstrates understanding of the visualization techniques.
Within one month of launching the course, over two hundred people enrolled in the course. One of the most significant insights was that only 25% of learners got past the first module. However, once past the first module, 65% completed the second module. The second module, while much shorter, was also much more interactive. Most people clicked on interactive elements if they were present, even if they were not required to progress to the next slide. After digging deeper into the data to see exactly which slides learners failed to complete in the first module, I designed a series of interactive exercises to preempt such slides in order to increase overall completion of module one.
As of December 2016, over 600 people have enrolled in the course. We continue to analyze our Learning Records Store data to see how improvements could be made. We also ask learners to complete a qualitative course evaluation. Between these evaluations and the data we receive, this course is in a constant state of iteration in order to deliver the best possible learning experience for students.