Digital Badges & Credentials: How to Get the L&D Data You Need

In this blog post, we’ll focus on why a robust data integration is critical when it comes to badging and credentialing software. We’ll also explain how you can extract that data so you can combine it with other L&D data and report on it in a learning analytics platform (LAP).

What’s digital credentialing software, and how does it fit into a learning ecosystem?

In 2012, the Mozilla Foundation (the organization behind Firefox web browser) and their partners published a standard called Open Badges that described how to use digital badges to reward and recognize lifelong learning achievements.

These badges were images with embedded metadata about who had earned them and how, and they could be displayed in a badge backpack, or “badgepack.”

The project was a recognition that outdated models of workforce learning—where people attend college and then work in the same sector their whole careers—have changed. These days, the modern workforce continuously learns, grows, and adapts in response to developments in skill demand.

Initially seen as a bit of a gimmick and a way to “gamify” learning, digital badges (or digital credentials as they are sometimes known) have grown up a lot during the last decade. In addition to standard certifications and qualifications, organizations are now using badges as a serious and authoritative recognition of workplace skills and knowledge that’s learned and applied.

Digital credentialing software operates in the backend of the learning ecosystem and awards credentials to learners for activities:

• in other systems, or
• outside of the learning ecosystem.

These credentials are often displayed in other learning systems—such as the LMS or LXP—or on social media sites like LinkedIn.

Learners may still access the credentialing software to manage their profiles or share their credentials (though they might access the credentialing software via SSO from the LMS or LXP and not realize they have moved from another system).

The digital credentialing software is, therefore, heavily reliant on data from other systems to award credentials. Credentials may be awarded automatically via a technical integration with one of the systems that holds the data (here are some examples of Credly’s integrations).

However, this also can happen manually—when someone involved in the final stages of a process reviews the data or observes the learner decides whether or not to award a credential.

Digital credentialing data requirements and what to ask your vendor

Primarily, the role of the credentialing software is to issue credentials, so the most important data to capture is about what credentials were issued to whom and when. Beyond that, data relating to the following events also may be available and valuable:

• When a credential expires or is canceled
• When a learner shares a credential on an external system, such as LinkedIn
• When somebody clicks on a credential shared on an external system
• Other learner activities on the credentialing platform

^{This is an example dashboard illustrating the types of insights you might uncover from badging data. It includes reports relating to badges earned, badges shared, and the competencies that badges represent.}

Data from the credentialing software can tell you about learners, skills in your organization, and utilization of the credentialing software.

• Learners: Data can be used to display a record of credentials earned at both individual and team levels.
• Skills: Data can be used to search for skills within the organization (based on who has earned specific credentials) or report on the organization’s prevalence or lack of particular skills.
• Utilization: Data can be used to see how well people are utilizing the credentialing software.

Remember, a good xAPI implementation is ideal for getting data from any system into your LAP. And automated CSV exports can be a good plan B where the first option is not available. When digital credentialing software doesn’t have xAPI tracking or automated CSV exports, you may be able to fetch data in a JSON format.

JSON is not inherently more difficult to work with than CSVs (and in fact, there are many benefits). However, because using JSON is unusual, LAPs generally don’t have built-in functionality to ingest and translate JSON data to xAPI.

This is by no means an obstacle, but it does mean that fetching the data requires a “connector” (i.e. a small application that pulls data from the source system, translates it to xAPI, and then pushes it to the LAP).

Data can flow both ways between the LAP and the digital credentialing software. Data about credentials can be used in reporting, while data about other learning activities, aggregated together by a learning analytics platform, can be used to inform the award of credentials.

But while you can use data from the LAP to automatically award badges via the credentialing software, it’s relatively technical to set up. That’s because it would require either:

• another connector, or
• use of an LAP feature that can automatically push data via API requests based on data from reports. (These features tend to be complex, and not all LAPs even have them.)

In other words, getting data out from and into the digital credentialing software requires either a developer with experience working with APIs, or calling in the experts (i.e. us!).

At Watershed, we’ve already developed a connector for Credly to work with one client’s requirements, and we’re working on developing this connector further for use by other clients. (See an example of how one client is using Watershed’s Multiple Dimensions to help with the rollout and adoption of badging within their organization.)

Because the integration between the digital credentialing software and learning analytics platform is a bit more complex than with other parts of the ecosystem, it’s essential to discuss this integration in detail with both vendors early on.

Ideally, you should get the vendors on a conference call to collaborate on the best solution and include this collaboration time in the RFP.

It’s also important to be clear on whether or not you want to issue badges based on data in the LAP automatically. Having the two systems integrated does not necessarily mean it is a two-way integration.

Additionally, consider asking the credentialing vendor the following questions:

• Does the vendor have experience integrating with an LAP or LRS? Specifically, does the vendor have any experience integrating with the LAP you use? (For example, Credly lists Watershed as an integration on their website.)
• Is data about badges issued and other interactions available via xAPI or CSV? As mentioned above, the answer to this is probably “no,” and that’s ok. However, it’s worth asking just in case.
• What data is available, and how can it be extracted?
• Is the data API well documented, and can the LAP vendor review that documentation?
• What APIs are available to automatically issue badges based on LAP data?

Digital credentialing software and your learning ecosystem

You can use credential data alongside data from other systems to paint a picture of how learners develop skills that go toward earning the credential. For example, you can ask the following questions (and more), which require data from both the credentialing software and other systems:

• What topics are learners, who have particular accredited skills, searching for?
• What content did learners complete in the period prior to earning a specific credential?
• What credentials do people in a certain job role have? Or what job roles do people with a particular credential have?

For this to work, the credentialing data needs to be mappable to the rest of your learning data. That’s why the credentialing software should use a mappable learner identifier.

In addition, this data might be helpful to improve your training offering, as it can help inform the curation of content collections meant to help prepare learners to earn a particular credential.

Changes to the ecosystem are unlikely to affect data relating to earned badges, but could significantly impact the process of awarding badges automatically.

For example, one criterion for awarding a particular badge is watching a video on the platform. But if you replace that platform, there is a risk that badge would no longer be awarded unless the data from the new platform exactly matches that of the old platform.

When replacing systems that have badge criteria associated with them, you need to consider either:

• how to keep the data consistent enough that the criteria is still recognized, or
• how you will update the criteria in the integration to ensure the badge is still awarded.

The role of badging and credentialing in skills and compliance

Digital credentials are generally awarded for job-related skills. However, if badges are awarded for skills or knowledge relating to compliance, then data from the digital credentialing software could help show evidence of compliance.

And where the credentialing process is done well and at scale, the digital credentialing software can become your organization's primary skills data source.

For this to be effective, however, all relevant skills need to be represented as digital credentials—such that your list of badges can serve as a comprehensive skills taxonomy.

This approach is unlikely to be practical when starting with digital credentialing, as it will take time to develop a complete set of badges for your organization. Until you hit that point, data from the LXP may be more beneficial for skills reporting.

While having too few badges limits the usefulness of credentialing data for skills reporting, so can having too many! If too many similar skills have badges associated with them, it may not be possible to maintain sufficiently robust criteria to indicate the achievement of those skills accurately.

In that case, the badge data becomes less helpful in identifying who genuinely has the skills you require. Therefore, good credentialing governance ensures and assures the value of credentials issued by your organization is vital.

Up Next: Coaching and checklist data requirements

Data from digital credentialing software is valuable for evaluating the utilization of digital credentials and reporting on the prevalence of verified skills in the organization. Aggregated learning data also can be helpful to inform the award of credentials in the first place.

And a checklist application is one potential data source that captures data about skills demonstrated. So our next post will explore the data requirements for observation checklist applications and consider how you can use this data in L&D reporting.