Real-Time Learning Analytics

The $2.3 billion question nobody in government training wants to answer: "Are your learners actually learning?" I've spent years in DoD and IC training environments, and here's what I consistently hear: "We hit 95% course completion rates." But completion ≠ comprehension. The uncomfortable truth? Most learning management systems track seat time and clicks—not whether knowledge transferred to long-term memory or whether learners can apply new skills to their roles. At FedLearn, our AI analyzes 250+ behavioral data points to predict knowledge transfer in real-time with over 90% accuracy. We measure learning on a 0-100 scale as it happens—not weeks later through a multiple-choice test that learners can pass by process of elimination. Here's what this means practically: When a GS-13 intelligence analyst is struggling with a quantum computing concept in minute 14 of a course, our system knows it immediately. The content adapts. Additional resources surface. The learning path shifts—all autonomously. The alternative? That analyst clicks through, checks the completion box, and returns to their desk with a certificate but no capability. We built our platform because warfighters, intelligence professionals, and mission-critical personnel deserve better than checkbox training. They deserve learning that actually sticks. What would change in your organization if you could identify—in real time—which learners were falling behind before they ever failed?

“The day we turn the reactor on—with a thousand sensors—AI will be learning from the reactor as it goes online.” This line from TerraPower’s Christopher Levesque points to a fundamental shift in how we approach nuclear reactor startups. The Natrium plant is designed with detailed multiphysics models—essentially a full digital twin of the plant. So when the reactor starts for the first time, the control system won’t just be logging data. AI will be processing real-time streams from thousands of sensors, learning the plant’s unique behavior. It’s as if the reactor comes with an embedded AI operations co-pilot, getting smarter by the second. Consider what an AI-sensor fusion architecture like this involves. On day one, sensors across the plant—temperature probes, flow meters, neutron detectors, strain gauges—stream high-fidelity data into an analytics core. Instead of just flagging values out of range, the AI cross-analyzes patterns, refining a live model of plant behavior. This creates a tight feedback loop. As the reactor heats up and settles into operation, the AI compares predictions to actual performance, continuously calibrating the plant’s digital twin. Expected outcomes: • Enhanced safety: With AI watching a thousand inputs, anomalies surface early, giving operators time to intervene before small issues escalate. • Operational efficiency: A learning reactor is an optimizing reactor. Real-time insights enable smoother startups, lower transients, and reduced wear. • Scalable learning: Insights from one startup can improve the next. Over time, the fleet grows smarter with every iteration. This is nuclear engineering meeting intelligent systems design. TerraPower’s Wyoming project will show us what happens when real reactors start learning. And the faster those insights enter industry practice, the more transformative this becomes. Dive deeper into TerraPower’s vision and the role of AI in nuclear energy: https://lnkd.in/e4aF9Xu9 #NuclearEnergy #AdvancedReactor #ArtificialIntelligence #DigitalTwin #EnergyInnovation

I wrote this guide for data scientists who are used to working with static datasets and batch jobs but want to start working with real-time data. It covers the fundamentals of working with real-time data, why streaming matters for ML, and how to use tools like Kafka, Flink, and PyFlink to build streaming pipelines. Includes end-to-end examples: – Real-time anomaly detection – Thematic analysis with GPT-4 – Online prediction and monitoring 📖  Check it out: https://lnkd.in/gybD2z8q

💡 What if your LMS data could reveal how your students learn—not just what they get wrong? In this study published in the Journal of Educational Psychology, Bernacki et al. (2025) used multimodal learning analytics to decode students’ “digital traces”—the clicks, downloads, and submissions that quietly capture how they self-regulate learning. By aligning digital behaviors with students’ think-aloud reflections, the team found patterns that not only validated these traces as indicators of self-regulated learning (SRL) but also predicted performance across semesters. This research points toward a future where real-time learning data can flag struggling students before they fail—and guide instructors to target the why behind the struggle. 🔗 https://lnkd.in/eFaQttau #LearningAnalytics #EducationResearch #EdTech #StudentSuccess #HigherEd