Data Instrumentation: The Work That Makes Analytics Possible
At Yet Analytics, we have approached data instrumentation from multiple directions: publishing activity natively from Unity applications, translating Moodle platform events, instrumenting media playback in VLC, and observing distributed simulation activity through Federate xAPI. These projects differ in scale and architecture, but they share the same objective to make activity interoperable, accessible, and actionable.
WEBINAR ANNOUNCEMENT - Federate xAPI: Bring HLA Simulation Data into the xAPI Ecosystem
In this webinar, we'll introduce Federate xAPI, an open source capability that bridges HLA simulation and xAPI. Join us for a live demo and conversation.
Federate xAPI: Bring HLA Simulation Data into the xAPI Ecosystem
Federate xAPI is an Apache 2.0 open source HLA federate that transforms simulation activity into Experience API (xAPI) data without requiring changes to your existing simulation federates.
Yet Analytics Announces Federate xAPI, Bringing Low-Code xAPI Instrumentation to HLA Simulations
Federate xAPI is a low-code HLA federate that can be added directly to an existing Runtime Infrastructure (RTI) to begin transforming simulation activity into xAPI statements.
Join Us for a Webinar: AI Ethics in Learning, From Principles to Practice
Join us on Jul 9, 2026 at 10AM Eastern Time, for a free webinar featuring Jeanine DeFalco, PhD and Shelly Blake-Plock. In addition to celebrating the publication of IEEE 2247.4-2025, the IEEE Recommended Practice for Ethically Aligned Design of Artificial Intelligence in Adaptive Instructional Systems, we’ll be talking about how to leverage ethical frameworks to increase value within organizations.
AI Ethics for Learning: Moving Beyond Principles to Practice
From industry and workforce development programs, to government and non-profit deployments, to academic rollouts across higher education, we seek to guide organizations in frameworks for the design and implementation of ethical AI that are accessible, meaningful, and actionable. The result will increase value to customers, provide a measurable cultural impact, and present a clear and defensible return on investment.
What Comes After ADL? A Practical Continuity Check for Learning Data
With ADL now closed, a lot of organizations are facing the same quiet but important question… what happens next?
Your Learning Data Is Organizational Intelligence. Treat It That Way.
Most learning data systems were not designed to work together. In some cases, they were actively designed to not work together.
Why Your Learning Data Is Not Ready for AI (and how Learning Engineering can solve this)
Every organization is talking about AI.
Some are piloting tutors. Some are experimenting with generative feedback. Some are imagining adaptive learning pathways, automated coaching, predictive analytics, and personalized workforce development at scale. But the uncomfortable truth is that most learning data is not ready for AI.
That does not mean organizations lack data. In fact, many have too much of it. They have LMS completion records, assessment scores, survey responses, course metadata, content usage reports, platform logs, HR records, credential data, simulation outputs, and dashboard exports. The problem is not the absence of data. The problem is that most of this data was never designed to work together, and certainly was not meant to be consumed by AI.
Turn Learning Data Into a Strategic Asset
Organizations are investing heavily in learning technologies, including LMSs and LXPs, simulations, AI-enabled platforms, immersive environments, and more. The expectation is clear: better training, better performance, better outcomes.
But there’s a problem.
Despite this investment, most organizations still struggle to answer basic questions about their learning systems.
A Decade of AI in xAPI: Building, Not Chasing
For the team at Yet Analytics, AI in the xAPI ecosystem isn’t a recent addition or a repositioning. It’s been a continuous line of inquiry, development, and application that stretches back more than a decade. We’ve been instrumenting AI systems well before the current moment made “AI-powered” a default descriptor.
What If Your Case Studies Could Talk Back?
We’re opening up a new opportunity to explore AI-powered simulations to a small group of higher ed instructors.
Participants will get 90 days of access to the Mixta authoring platform, along with a strategy session led by a learning scientist. The goal is simple: build and run a simulation in a real course.
Build Capable and Yet Analytics Announce Partnership to Bring Learning into the AI Age… Without the LMS
Build Capable and Yet Analytics have announced a new partnership aimed at helping organizations design, deliver, and scale learning in the AI era without relying on a traditional Learning Management System (LMS).
Three Ways Mixta Changes the Game for Simulation
Mixta is one of the few things that actually feels like a step forward, not because it adds AI to simulation, but because it changes what simulation is.
Meet Mixta: AI-Powered Simulation Built through Learning Engineering
Mixta is one of the most compelling advances in simulation for the learning space that we’ve seen. It was designed by learning scientists and was developed through a learning engineering mindset.
We think that by this time next year, it will be the most commonly used simulation platform in the learning and training space.
SQL LRS Is Widely Adopted. Now Let’s Talk About What That Means.
Many organizations today are running SQL LRS in production environments.
That means it is collecting and processing mission-critical data, feeding downstream analytics and reporting systems, and supporting compliance, certification, and operational decision-making.
In other words, SQL LRS is not a side tool. It is part of the system of record.
At the same time, many of these deployments are operating without a formal support relationship.
That creates a gap. And it’s one that’s easy to overlook until it matters.
SQL LRS and the Future of Learning Data: From Storage to Intelligence
We are now operating in an AI-driven landscape where raw activity data is no longer sufficient. Logging events is easy. Extracting meaning is hard.
Machine learning systems don’t need more noise—they need:
Structured signals
Verified outcomes
Aggregated performance
Deterministic logic
In other words, they need preprocessed intelligence.
SQL LRS is designed to deliver exactly that.
Free Is Not Free: The Hidden Cost of “Free” Infrastructure
Entire ecosystems of modern software now advertise free tiers as the starting point for adoption. For experimentation and early development, these offerings can be incredibly useful. They lower barriers, encourage exploration, and help teams get projects off the ground quickly.
But there is a subtle shift that happens as systems mature. What begins as a convenient free tool can gradually become something far more significant: the place where an organization’s data lives.
At that point, the economics of “free” start to look very different.
Because infrastructure—real infrastructure—has never actually been free.
Two Game-Changing AI Advantages of SQL LRS
For organizations working with learning and performance data, the biggest barrier to using AI effectively is rarely the models themselves—it’s the friction between data collection, transformation, and usable features.
This is where SQL LRS creates a significant advantage.
From LMS Lock-In to Enterprise Analytics: Unlocking Partner Training Data with SQL LRS and LRSPipe
A few years ago, big LMSs started shipping with the availability of their own internal Learning Record Stores. But more often than not, this only resulted in more frustration. Because even when the LMS captured rich activity data using xAPI, the data remained locked inside the system’s built-in LRS. Just getting access to your own data became an endless game of phone-tag.
So, while the training data exists, businesses can't easily use it.
This is where LRSPipe and SQL LRS provide a powerful solution.