Data Storytelling: How to Change L&D Behavior by 'Reporting Out Loud'

By David Rosenfeld | Sep 17, 2019

In this post, David Rosenfeld of athenahealth shares how he used “Reporting Out Loud” to encourage a behavior change within an L&D team so learning analytics isn’t just icing on the cake, but rather, it’s baked into their strategy and programs.

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5 Ways the Learning Analytics Landscape Is Evolving

By David Ells | Sep 12, 2019

After combining the data from annual learning measurement surveys with L&D practitioner stories shared during Insights Summits, we’ve noticed a growing shift in the way both L&D professionals and the organization as a whole view learning analytics. That is, L&D doesn’t just want to measure learning's impact, the broader organization is actually prioritizing it. In this post, we’ll discuss the meaning behind these trends and how L&D departments can continue to develop their measurement practices.

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3 Tips for Implementing a Learning Analytics Practice

By David Ells | Aug 28, 2019

A few years ago, we explored why L&D should use data and analytics in data storytelling. Since then, we've seen practitioners go from concept and ideas to storytelling in practice. Over the next several posts, we’ll dive into how real organizations view learning analytics and how they are using their data narratives to gain buy-in, effect change, and showcase improvement across L&D initiatives.

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5 L&D Lessons from Stranger Things [No Season 3 Spoilers]

By Andrew Downes | Jul 24, 2019

The recently released third season of Stranger Things has been Netflix’s most successful original programming to date. And that’s no strange thing (pun absolutely intended), given the popularity and success of the first two seasons. That’s why we couldn’t resist writing a Stranger Things-themed blog post (just like Will Byers couldn’t resist The Mind Flayer). So expect tenuous links and terrible puns as we ask: What five things can this hit show teach us about Learning and Development?

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What Are Corporate Learning Analytics? [Recap]

By Andrew Downes | Jun 19, 2019

Learning Analytics seem like a bit of a dark art, a mystery. You’ve read the theory, but how does it actually work in practice? It’s this question that inspired us to conduct our Learning Analytics Research Study so we could better understand how our clients are implementing these analytics in practice.

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Learning Analytics: Report Types

By Andrew Downes | May 14, 2019

We’ve looked at categories. We’ve looked at complexities. We’ve looked experience types. There’s only one direction left to slice and dice findings from our Learning Analytics Research Study—which looked exclusively at how clients use Watershed, including report types. In this post, we’ll explore which report types organizations use most often and then compare how these organizations use different report types.

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Learning Analytics: Experience Types

By Andrew Downes | Apr 24, 2019

So far in this series, we’ve looked mainly at categories and complexities within the learning analytics triangle. This week, we’ll look at learning analytics from another angle: What sort of learning activities are organizations collecting and analyzing data about? Join us, as we explain more and highlight several interesting finds along the way.

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Learning Analytics: Predictive & Prescriptive Complexity

By Andrew Downes | Apr 10, 2019

The highest complexity of learning analytics is predictive and prescriptive analytics. This means using data to either predict what might happen, or to prescribe learning activities. And, out of the 5,713 reports we reviewed as part of our Learning Analytics Research Study, we only found a fraction of reports that we classed as “Predictive and Prescriptive.” In this post, we’ll explain how these reports are being used and suggest other ways you can try predictive or prescriptive analytics in your organization.

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Learning Analytics: Advanced Evaluation Complexity

By Andrew Downes | Mar 26, 2019

Advanced Evaluation is less common than Data Evaluation, which means there’s no well-worn path leading the way. Organizations venturing into this level of complexity are often doing so in ways that are different from others. We see this in our Learning Analytics Research Study data, where there isn’t one analysis type that has significantly more report views than the rest. That's why we’ve divided the Advanced Evaluation complexity into five analysis types, which we’ll explore in this post along with examples of what organizations are doing in terms of this complexity.

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Learning Analytics: Data Evaluation Complexity

By Andrew Downes | Mar 12, 2019

Last week, we saw that Data Evaluation is by far and away the most common complexity level of Learning Analytics we observed in our research study. Within this complexity, we found 16 types of analysis represented, which we’ve bundled into three headings and will explore in this blog post.

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