Presentation by Zach Posner delivered to learning & development professionals across the globe.

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
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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?