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Modular, Data-driven,
Adaptive: Future of
Training
From the learning science company
ATD Webinar
Zach Posner
Zach.Posner@mheducation.com
Not much has changed…
2©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Image source:
https://nkilkenny.wordpress.com/category/blogs/
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
Modular content is everywhere
Finish
What does this mean for learning?
Vs.
Start
Finish
Start
Finish
Start
Finish
Learners experience personalized and variable
learning paths
Vs.
Start
Finish
Modular content becomes data-driven
Music Social Media
Videos/Movies Books
What is the big difference between learning and
entertainment?
Entertainment
Learning
Like Dislike Star
Rating
Time Tagging
Factors
Demographic
Factors
Framework for understanding learning…
9©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Learning
objective
AssessmentLearning
content
Learning objective
10
©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Each learning objective is aligned with a resource
Learning content
11
©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
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.
Assessments are key
13©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Rich and varied
learning content
Robust
assessments
?
?
?
?
Modular
Content
Assessment
Driving personalized learning paths with data
Modular
Content
Assessment
Driving personalized learning paths with data
Data used to optimize each learner’s experience
Entertainment
Learning
Like Dislike Star
Rating
Time Tagging
Factors
Demographic
Factors
Accuracy Confidence Time Cohort
Trends
Personalized and variable paths for each learner
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
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.
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
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
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
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
Powerful data layer with advanced analytics
Agile
Authoring
Tailored
Instruction
Data Layer
Your
Content
Our
Platform
Personalized
Learning
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
MHE
COMPLIANCE
ONBOARDING
CERTIFICATION
Improvement across all applications
Safety Sales TechnicalLeadership
Your
Content
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
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
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
We exist to unlock the full potential of every learner
30
31Img source: http://www.wrightslaw.com/nltr/16/nl.0105.htm
Questions ?
Write to me at
Zach.Posner@mheducation.com

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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 ? ? ? ?
  • 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