So far in our xAPI Governance blog series, we’ve looked at what you can do to protect against bad xAPI data and how you can fix it. In part 6 of the series, we explore how using a flexible learning analytics platform can help you work with imperfect L&D data, reducing the impact of xAPI Governance failures.
Now, before I start, let me be clear: You shouldn’t ever rely solely on your learning analytics platform to maintain good xAPI data. You need good governance as well.
Keeping your data clean and consistent is always the best approach, but it doesn’t hurt to have a plan B. For instance, a flexible LAP can act as that plan B when:
- data issues slip through the net; or
- you need to work with a data source that can’t conform to your governance rules.
Flexible Vocabulary
The most important feature of a flexible, xAPI-conformant LAP is its ability to work with any xAPI data, regardless of the identifiers used and the structure of extensions.
While an LAP might provide reports designed for specific use cases and types of data, an LAP also needs to be flexible enough to work with the broad mix of learning data that xAPI can support.
Otherwise, you might only be able to partially report on data sources—or worse, you might not be able to report on some data sources at all.
A flexible LAP will enable you to filter and organize reports by any verb and activity type as well as report on any data type held in an extension.
If your LAP requires a particular data structure for reports to work, then it’s unlikely your LAP will be able to support all your requirements—especially as your xAPI implementation grows.
Recommended Reading
Persona Association
In an ideal world, all your xAPI data will use a single persona identifier that’s unique to each learner and never re-used for a different learner. In practice, organizations might often use systems that assign different identifiers for learners in addition to a central system that maps those various identifiers together.
So where does that leave your xAPI data that uses multiple identifiers for each person? And how can you join that data together?
A flexible LAP will include functionality to alias all these persona identifiers under a single person so you can report on the data together. Watershed, for example, can import a mapping of identifiers as a CSV file (normally from an HR system) in order to link the data in this way.
Recommended Reading
Identifier Aliasing
Occasionally we see data where multiple activity IDs have been used for what should be a single xAPI activity (e.g. activity IDs that use different capitalization are considered totally separate, even if they’re used for the same activity).
We also see cases when multiple verbs with effectively the same meaning are used to describe multiple actions. Using multiple activity IDs to refer to one activity or several verbs to describe one thing can make reporting difficult.
That’s because you really want to see these appear as a single thing rather than multiple items. A flexible LAP will include functionality to treat multiple activity IDs as a single activity as well as multiple verbs as a single verb.
The LAP shouldn’t rely on changes to the underlying xAPI data, which is immutable; however, from a reporting perspective it should be as though the underlying data had been changed to merge the identifiers.
Definition Editing
A common problem when SCORM data is converted to xAPI is that it only includes identifiers—while leaving out names, descriptions, activity types, or other activity definition metadata.
Data from other sources also may include activity definitions with missing or misleading data.
A flexible LAP includes functionality to edit activity definitions. xAPI defines LRS functionality to maintain a canonical definition of each activity ID. The ability to edit that canonical definition, which is what should be used in reports, enables you to fill in any blanks and correct errors in those definitions.
The ability to edit activity type and verb names can also be useful.
The canonical format requests the LRS to return its own internal definitions of objects, rather than those provided in the statement.
If you trust the LRS, this is normally the most appropriate format when the data will be displayed to the end user. The LRS will build its internal definitions of objects based on statements it receives and other authoritative sources.
That’s all folks!
And this brings us to the end of our series on xAPI Governance. As a reminder, here’s a summary of the series and how to do xAPI Governance well.
- What's xAPI Governance?
- Have processes and rules.
- Document your xAPI statements.
- Test, monitor, and enforce.
- Be ready to fix or remove bad data.
- Use a flexible Learning Analytics Platform.
NOTE: Fight Club is used here only to illustrate the examples in this blog post. Watershed is not associated with, sponsored by, or affiliated with Fox 2000 Pictures or Regency Enterprises.
About the author
As a co-author of xAPI, Andrew has been instrumental in revolutionizing the way we approach data-driven learning design. With his extensive background in instructional design and development, he’s an expert in crafting engaging learning experiences and a master at building robust learning platforms in both corporate and academic environments. Andrew’s journey began with a simple belief: learning should be meaningful, measurable, and, most importantly, enjoyable. This belief has led him to work with some of the industry’s most innovative organizations and thought leaders, helping them unlock the true potential of their learning strategies. Andrew has also shared his insights at conferences and workshops across the globe, empowering others to harness the power of data in their own learning initiatives.
Subscribe to our blog
