Driving Training ROI Training Network Magazine
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Driving Training ROI Training Network Magazine
- 1. Driving Training ROI with Modular, Data- Driven, Adaptive Learning Presented by Zach Posner SVP & Managing Director Learning Science Platforms Mheducation.com/platforms lsp@mheducation.com
- 2. 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 Evolution of Corporate Learning Source: BERSIN
- 3. Next Milestone: Mastery-Based, Adaptive 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
- 4. Mastery-Based, Adaptive: Contrast with Conventional model 4©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Conventional model of learning Everyone given the same amount of time to learn same thing Emphasis on final assessment Everyone given same instruction Image source: https://nkilkenny.wordpress.com/category/blogs/
- 5. Conventional learning standard outcomes #oflearners Mastery level Conventional learning
- 6. Basic Differences: Mastery vs. Conventional Learning 6©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Mastery- model Conventional model of learning Everyone given the same amount of time to learn same thing Emphasis on final assessment Each learner progresses at own pace Everyone must achieve mastery before moving on to next objective No one can give up Everyone given same instruction
- 7. Conventional vs. mastery learning outcomes #oflearners Mastery level Conventional classroom Mastery learning
- 8. If progress depends on mastery, what is mastery? Precise and meaningful learning objectives 8©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
- 9. If progress depends on mastery, what is mastery? Precise and meaningful learning objectives Rich and varied learning resources 9©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
- 10. If progress depends on mastery, what is mastery? 10©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Precise and meaningful learning objectives Robust assessments Rich and varied learning resources
- 11. Learning objective 11 ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Each learning objective is aligned with a resource
- 12. Learning resource 12 ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
- 13. Assessment 13 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.
- 14. Mastery learning: the role of assessment 14©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Rich and varied learning resources Robust assessments Summative assessment
- 15. Adaptive = mastery + personalization + scale 15©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Adaptive Learning Digital Mastery based Personalized Print Seat-time One-size-fits-all
- 16. Learning resources Assessment Adaptive = mastery + personalization + scale
- 17. Learning resources Assessment Adaptive = mastery + personalization + scale
- 18. Mastery meets personalization and scalability
- 19. Each learner reaches mastery in their own way
- 20. Each learner reaches mastery in their own way
- 21. Each learner reaches mastery in their own way
- 22. We use data to personalize content in our consumer lives Music Social Media Videos/Movies Books
- 23. How can we use data to personalize learning content? Entertainment Learning Like Dislike Star Rating Time Tagging Factors Demographic Factors
- 24. Learning theories produce holistic mastery ©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 24References available upon request
- 25. Concrete product application for each learning theory 25 ©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.
- 26. Data used to optimize each learner’s experience Entertainment Learning Like Dislike Star Rating Time Tagging Factors Demographic Factors Accuracy Confidence Time Cohort Trends
- 27. Adaptive learning scales one-to-one learning 27©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. One-to-one mastery learning Conventional Learning Mastery Learning #oflearners Mastery Level http://web.mit.edu/bosworth/MacData/afs.course/5/5.95/re adings/bloom-two-sigma.pdf Adaptive learning scales
- 28. What does this mean at the organizational level?
- 29. Beyond the individual: unlock organizational performance 29 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
- 30. Beyond the individual: unlock organizational performance 30 Measurement Mastery Efficiency Engagement Agility Advanced Analytics Trends Strengths Opportunities Proficiency Organizational Readiness Time to Train Redistributed Investment Direct Opex Savings Productivity Risk Avoidance Retention Personalized Paths Experience Reduced Versioning Real-time Revisions Feedback Measurement Mastery Efficiency Engagement Agility
- 31. 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% 31
- 32. Powerful data layer with advanced analytics Agile Authoring Tailored Instruction Data Layer Your Content Adaptive Platform Personalized Learning
- 33. MHE COMPLIANCE ONBOARDING CERTIFICATION Improvement across all applications Safety Sales TechnicalLeadership Your Content
- 34. 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
- 35. Learners take ownership of their learning RECHARGE 0% 0% 0% 0%
- 36. Trainers and managers make learning actionable through data ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. 36
- 37. Authors and subject matter experts can continually refine content 37©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. Authors see real-time data on the effectiveness of content and questions Learners can offer feedback 70%
- 38. MHE adaptive leverages investment in science and technology 38 $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
- 39. Benefit from high-touch, customizable support 39©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. We do it for you We do it together You do it with our support Your Company 1 2 3
- 40. Turn-key pilot packages 40©2017 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. • Turn Key • Cost Effective • Non-Disruptive Full Program Management and Training Complete Program Enablement Quality First Program Build On-going Program Support Custom Integration Planning
- 41. We exist to unlock the full potential of every learner 41 We exist to unlock the full potential of every learner Questions?