Science Learning Analytics

I was reviewing quarterly reports with a client last month when they asked me a question that stopped me in my tracks: "Scott, we have all this learning data, but I still don't know which programs are actually improving performance." After 12 years as CEO of Continu, I've seen firsthand how organizations struggle with this exact problem. You're collecting mountains of learning data, but traditional analytics only tell you what happened - not why it matters. Here's what we've learned working with thousands of organizations: The real value isn't in completion rates or assessment scores. It's in the connections between those data points that remain invisible without the power of tools like AI. One of our financial services clients was tracking 14 different metrics across their onboarding program. Despite all that data, they couldn't explain why certain regions consistently outperformed others. When we implemented our AI analytics engine, the answer emerged within days: specific learning sequences created knowledge gaps that weren't visible in their traditional reports. This isn't just about better reporting - it's about actionable intelligence: - AI identifies which learning experiences actually drive on-the-job performance - It spots engagement patterns before completion rates drop - It recognizes content effectiveness across different learning styles Most importantly, it connects learning directly to business outcomes - the holy grail for any L&D leader trying to demonstrate ROI. What's your biggest challenge with learning data? Are you getting the insights you need or just more reports to review? #LearningAnalytics #AIinELearning #WorkforceDevelopment #DataDrivenLearning

🚀 Can teaching students “how to learn” actually change how they engage with their coursework? In this study published in the British Journal of Educational Technology, we used over 257,000 online learning “clicks” from biology students to track how their study habits evolved. Researchers moved beyond simply counting clicks—they mapped patterns of engagement, like how regularly students moved between different resources (quizzes, notes, calendars). Key findings: Students who received a short “science of learning to learn” training showed more organized, regular study patterns—and kept them up all semester. This regularity (think: consistent, purposeful learning routines) was a strong predictor of final grades—above and beyond just how much students clicked. Complexity-based network analysis offers powerful, AI-ready ways to monitor and support student self-regulated learning in real time. 💡 The big idea: Success isn’t just about what you study—it’s about building adaptive, organized habits you can sustain. https://lnkd.in/er9mmBfa

A new publication from our group about #interactivetextbooks and #learninganalytics in the journal Chemical Engineering Education published by the ASEE Chemical Engineering Division and AIChE Education Division. The title of the work is: Interactive Reading and Auto-Graded Homework Analytics and Correlations for Multiple Cohorts When Using an Interactive Textbook for Material and Energy Balances. We analyzed 7 years of student data using the Material and Energy Balances zyBooks: A Wiley Brand. Yes, seven years is a very long time to do a research study and track trends and find correlations. We feel that interactive textbook has many robust features that lead to student success in #chemicalengineering and the MEB course. Great work by undergraduate researchers Samantha Yanosko and Grant Valentine from The University of Toledo Chemical Engineering Department . You can download the full paper at: https://lnkd.in/gbXMY3ck