In our last blog post, we introduced our latest series that focuses on findings from our Learning Analytics Research Study—during which we collected and analyzed data to see how real organizations are implementing learning analytics. We're kicking things off by sharing insights from overall Watershed report usage and how certain times of the year can affect both learning data and reports of different types and categorizations.
Beware of seasonal data patterns.
In the following example that spans from January 1 to November 13, 2018, you’ll see how seasonal patterns, such as key financial dates, can affect how and when organizations create and use reports.*
In this example, Org 1 uses Watershed mainly for tracking people who have completed required courses. As a result, managers view reports to see completion statuses for each of their respective teams.
Notice that Org 1 had significantly more overall report views (i.e. Interaction Count) than other organizations, even though Org 1 didn't have a particularly high number of reports in use (i.e. Report Count).
In fact, Org 1 had an average of 172 views per report, and had one report with 4,893 views during that same time period. By comparison, others organizations collectively averaged 13.5 views per report.
As we continue to look at our report view data throughout this blog series, keep in mind there may be a bias toward this particular example because we have so many report views from Org 1. In particular, the following image illustrates how the focus on completions of required training results in significantly more report views around April (i.e. the end of the financial year) as managers keep track of people who are overdue for training.
We also noticed that after we identified our 5,713 reports to research, their report views started to immediately decline. This happened in conjunction with a continued increase in overall report views in Watershed (see the following image).
This suggests the number of views for existing reports declines over time as new reports are created and viewed—which is a good reminder to maintain and refresh reports as needed.
Remember, seasonal patterns are something to be aware of in your own reporting. So, expect numbers to fluctuate. And, once your data collection passes the one-year mark, you'll be able to start building a model to project expected seasonal numbers. Here are three actions you can take away from this blog post:
- Make sure managers can view and track the completion status of their team members.
- Expect spikes in activity around key dates and deadlines (e.g. year end).
- Don’t let reports go stale. Keep them up to date to ensure stakeholders remain engaged.
Up Next: Learning Analytics Categories & Dimensions
Join us next week as we begin to revisit the learning analytics model and review a couple new categories we’ve identified. You don’t want to miss out, so sign up for Watershed Insights to have the next blog post delivered right to your inbox.
*Disclaimer: All client names have been anonymized.
About the author
As one of the authors of xAPI, Andrew Downes has years of expertise in data-driven learning design. With a background in instructional design and development, he’s well versed in creating learning experiences and platforms in corporate and academic environments.
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