ATD Webinar: Modular, Data-Driven, Adaptive: Future of Training
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This document discusses the future of training and learning moving towards more modular, data-driven and adaptive models. Key points include: - Consumer media is increasingly seamless, data-driven, gamified, social, micro-integrated and personalized in a cloud-based format. Learning models should follow this trend. - Modular content paired with robust assessments can drive personalized learning paths optimized by data on factors like accuracy, confidence, time and cohorts. - An adaptive learning approach informed by learning science theories can lead to better learner outcomes like higher mastery levels across more learners compared to conventional learning models. - Data and analytics provide benefits for all stakeholders in learning including improved efficiency, engagement and organizational
ATD Webinar: Modular, Data-Driven, Adaptive: Future of Training
- 1. Modular, Data-driven, Adaptive: Future of Training From the learning science company ATD Webinar Zach Posner Zach.Posner@mheducation.com
- 2. Not much has changed… 2©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Image source: https://nkilkenny.wordpress.com/category/blogs/
- 3. But consumer media looks like this now… Copy Text Copy Text SEAMLESS DATA-DRIVEN GAMIFIED SOCIAL MICRO INTEGRATED HUMAN-CENTRIC PERSONALIZED JUST-IN-TIME CLOUD-BASED
- 4. Modular content is everywhere
- 5. Finish What does this mean for learning? Vs. Start Finish Start Finish
- 6. Start Finish Learners experience personalized and variable learning paths Vs. Start Finish
- 7. Modular content becomes data-driven Music Social Media Videos/Movies Books
- 8. What is the big difference between learning and entertainment? Entertainment Learning Like Dislike Star Rating Time Tagging Factors Demographic Factors
- 9. Framework for understanding learning… 9©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Learning objective AssessmentLearning content
- 10. Learning objective 10 ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Each learning objective is aligned with a resource
- 11. Learning content 11 ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
- 12. Assessment 12 Below is a screenshot from SmartBook (the learner tool). It probes the learner’s mastery of the underlying learning objective linked to the learning resource. ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
- 13. Assessments are key 13©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Rich and varied learning content Robust assessments ? ? ? ?
- 14. Modular Content Assessment Driving personalized learning paths with data
- 15. Modular Content Assessment Driving personalized learning paths with data
- 16. Data used to optimize each learner’s experience Entertainment Learning Like Dislike Star Rating Time Tagging Factors Demographic Factors Accuracy Confidence Time Cohort Trends
- 17. Personalized and variable paths for each learner
- 18. Learning science drives MHE technology, informs our design ©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Metacognitive Theory The Theory of Deliberate Practice The Theory of Fun for Game Design Ebbinghaus Forgetting Curve 18References available upon request
- 19. Product application for each learning theory 19 ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Deliberate Practice2 Theory of Fun for Game Designs4 Learning Science 1. Flavell, J. H. "Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry." American Psychologist (1979) 34, 906 - 911. Print. 2. Ericsson, K. Anders, Krampe, Ralf Th., Clemens Tesch-Romer. “The Role of Deliberate Practice in the Acquisition of Expert Performance.” Psychological Review Vol. 100 No. 3 (1993) 363-406. Print. 3. Ebbinghaus, Herman, Trans. Clara E. Bussenius and Henry A. Ruger Memory: A Contribution to Experimental Psychology. Eastford, CT: Martino Fine Books, 2-11. Print. 4. .Koster, Raph. A Theory of Fun for Game Design. Scotts dale, AZ: Paraglyph Press, Inc., 2005. Print.
- 20. Adaptive learning leads to better outcomes 20©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Adaptive Learning Conventional Learning Mastery Learning #oflearners Mastery Level
- 21. Beyond the individual: unlock organizational performance 21 Measurement Mastery Efficiency Engagement Agility When insight into learning is needed When compliance and true proficiency are required When real participation is necessary When learners are diverse in incoming skill and require individual paths When learning needs to translate into action or behavior change
- 22. Traditional and Computer-Assisted Training Instructor and Computer-based (CBT) LMS becomes the Administrative Platform The E-Learning Era Materials On-Line, Information vs. Instruction Blended and Informal Learning Mixing forms of media with informal learning Learning on demand with Integrated Programs Collaborative, Talent-Driven Learning Formalize Informal Learning Collaboration and talent management by Design 2009 + 2005 + 2000 + 1990’s The Evolution of Corporate Learning Source: BERSIN
- 23. The Evolution of Corporate Learning Traditional and Computer-Assisted Training Instructor and Computer-based (CBT) LMS becomes the Administrative Platform The E-Learning Era Materials On-Line, Information vs. Instruction Blended and Informal Learning Mixing forms of media with informal learning Learning on demand with Integrated Programs Collaborative, Talent-Driven Learning Formalize Informal Learning Collaboration and talent management by Design Mastery-Based Adaptive Learning Personalized, Competency-Based Data-Driven, Digital, Seated in Science 2016 + 2009 + 2005 + 2000 + 1990’s
- 24. Powerful data layer with advanced analytics Agile Authoring Tailored Instruction Data Layer Your Content Our Platform Personalized Learning
- 25. Benefits for every stakeholder • Eliminates course versioning • Real-time feedback for agile authoring LEARNER • Measureable reportable outcomes - advanced analytics • Improved efficiency and retention • Direct savings in cost to train • A personalized experience • Self-paced and easy-to-use • 100% mastery AUTHOR MANAGER/TRAINER
- 26. MHE COMPLIANCE ONBOARDING CERTIFICATION Improvement across all applications Safety Sales TechnicalLeadership Your Content
- 27. ROI: Possible metrics to evaluate learning ROI METRIC Measurement Mastery Efficiency Engagement Agility DESCRIPTION EXAMPLE Data layer allows stakeholders to identify trends (learning, content, and cohort analytics down to the objective) 100% mastery of all learning objectives (increase in proficiency rates, organizational readiness) Reduces training time (opex savings, increased productivity in redistributed full-time hours) Right content at right time makes learning inspire (increased retention, improved quantitative and qualitative survey data) Real-time analytics means real-time action for all stakeholders (leads to increased revenue, margin, market penetration) 75% of learners who exceed their sales quota are aware of their accuracy 80% of the time, in addition to achieving 100% mastery. 1000 learners are certified (achieved 100% mastery of 50 Learning Objectives, up from 75% mastery). 45% increase in efficiency due to transition from one-size-fits all to personalized learning. 90% of learners would recommend the course to others. 100% of learners completed the course, up from 50%. Learner data has been used to make course revisions and decreased the versioning time required by 30%. 27
- 28. Learners Case Study | Data improves learning efficiency Original Program 70 Min Adaptive Program Average 37 Min IT Services Industry Fixed 70 Min Webinar w/test Conversion to Adaptive Platform All Gained 100% Mastery Time / Opex Savings of 48% 28
- 29. MHE adaptive leverages investment in science and technology 29 $175M+ Investment per Year in Digital Platforms 2012 DPG Formed 2017 Growth via Investment 450 Engineers, etc 200 Engineer s, etc $ 1,600+ Adaptive products 4,000 Authors trained to use MHE Adaptive 5,000,000 Learners using MHE Adaptive 10,000,000,000 Data Layer Interactions MHE Adaptive Growth via Acquisition Growth via User Knowledge
- 30. We exist to unlock the full potential of every learner 30
- 31. 31Img source: http://www.wrightslaw.com/nltr/16/nl.0105.htm Questions ? Write to me at Zach.Posner@mheducation.com