The future of corporate learning is adaptive. Behavioral and performance data can be used to optimize every single moment in a learning experience, delivering the most efficient and effective training possible. This webinar will provide learning and HR leaders with the framework for making an investment decision in the adaptive learning landscape. Participants will develop a pragmatic understanding of the technology and understand how to benchmark different platforms, calculate ROI, and sell their investment decision forward to other stakeholders. In this webcast, learn how you can unlock performance and drive training ROI through adaptive learning technology. You’ll be able to: Recognize key trends in technology that allow learning to become modular, data-driven, and adaptive Grasp learning theories concerning focus, memory, metacognition, and engagement and how they can be used to unlock performance through adaptive technology Explore how to leverage the benefits of adaptive technology to drive top-line growth and bottom-line results

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Frequently
Asked
Ques%ons

5. Header is here
The Business Case
for Adaptive Learning:
Drive ROI and Unlock
Performance
From the learning science company
Presented by
Geoff Broderick

6. We exist to unlock the full potential of every
learner
6

7. • Adap've
learning,
what
is
it
and
how
does
it
work?
• Key
benefits
to
all
stakeholders
• I
want
it,
how
do
I
sell
it
internally
• Selec'ng
what’s
best
for
you
Discussion agenda
7

8. Do you face any of these learning challenges?
Mindset
Change
How
do
I
to
ensure
that
my
learning
ini'a'ves
contribute
to
organiza'onal
success?
We
need
to
move
beyond
training
for
training’s
sake.
Distributed
and
Diverse
Learners
I
have
learners
across
globe.
I
need
a
cost
effec've
way
to
deliver
training
any'me,
anywhere,
and
in
a
manner
that
mo'vates
and
challenges
both
my
novices
and
veterans
at
the
same
'me.
Time
&
Cost
Constraints
My
business
partners
con'nually
need
to
do
more
with
less.
I
need
a
way
for
my
learners
to
spend
less
'me
on
what
they
already
know
and
master
what
they
don’t
know.
Impact
We
need
data
to
confirm
that
the
learning
had
impact.
I
need
a
solu'on
that
provides
specific
data
on
learner
mastery
and
proves
a
strong
ROI.
Flexibility
We
need
to
make
frequent
updates
to
our
learning
content
for
changing
business
needs,
regula'ons,
policies
or
course
improvements.
How
can
I
do
this
rapidly
without
revising
an
en're
course?
8

9.
Adap've
learning,
what
is
it
and
how
does
it
work?
Discussion agenda
9

10. Tradi%onal
and
Computer-­‐Assisted
Training
Instructor
and
Computer-­‐based
(CBT)
LMS
becomes
the
Administra've
PlaVorm
The
E-­‐Learning
Era
Materials
On-­‐Line,
Informa'on
vs.
Instruc'on
Blended
and
Informal
Learning
Mixing
forms
of
media
with
informal
learning
Learning
on
demand
with
Integrated
Programs
Collabora%ve,
Talent-­‐Driven
Learning
Formalize
Informal
Learning
Collabora'on
and
talent
management
by
Design
2009
+
2005
+
2000
+
1990’s
Evolution of corporate learning
Source:
BERSIN
10

11. Next Milestone: Mastery-Based, Adaptive Learning
Tradi%onal
and
Computer-­‐Assisted
Training
Instructor
and
Computer-­‐based
(CBT)
LMS
becomes
the
Administra've
PlaVorm
The
E-­‐Learning
Era
Materials
On-­‐Line,
Informa'on
vs.
Instruc'on
Blended
and
Informal
Learning
Mixing
forms
of
media
with
informal
learning
Learning
on
demand
with
Integrated
Programs
Collabora%ve,
Talent-­‐Driven
Learning
Formalize
Informal
Learning
Collabora'on
and
talent
management
by
Design
Mastery-­‐Based
Adap%ve
Learning
Personalized,
Competency-­‐Based
Data-­‐Driven,
Digital,
Seated
in
Science
2016
+
2009
+
2005
+
2000
+
1990’s
11

12. Adaptive learning scales one-to-one learning
One-­‐to-­‐one
mastery
learning
Conven%onal
Learning
Mastery
Learning
#
of
learners
Mastery
Level
12
Source:
Bloom
2
Sigma
Problem
Adap%ve
learning
scales
one-­‐to-­‐one
learning

13. Mastery meets personalization and scale
13

14. Each learner reaches mastery in their own way
14

15. Each learner reaches mastery in their own way
15

16. 16
Adaptive technology uses AI to personalize learning for
each individual

17. Metacognitive
Theory
The Theory of
Deliberate Practice
The Theory of Fun
for Game Design
Ebbinghaus
Forgetting Curve
AI coupled with learning theory – Grounded in Science
17

18. Prove what you know, only learn what you do not know
Personalized
Learning
Path
Low
Stakes
Assessment
(Probe)
Learning
Resources
Data
18
Learning
Objec%ve
Text
Video
Audio

19. Learning
Goal:
Drive
a
Car
Defensive
Driving
Rules
of
the
Road
Opera%ng
a
vehicle
19
Creating modular content: Microlearning Objectives aligned with
Learning Resources and Probes

20. Align micro learning objectives with resources and probes
Dozens
of
Ques%on
Types
Micro
Learning
Objec've
Learning
Resource
Probes
Text
Video
Audio
Web
Link
20

21. New way to think about content assets
L
R
L
R
L
R
L
R
L
R
L
R
L
R
L
R
P P
P
P P
P
Database
of
micro-­‐assets
••••••
L
R
L
R
L
R
PP
P
Micro
Learning
Objec%ve
Micro
Learning
Objec%ve
?
?
?
L
R
L
R
L
R
P P
P
L
R
L
R
L
R
PP
P
?
?
?
Text
Web
Link
Audio
Video
Text
Web
Link
Audio
Video
21

22. Unlock performance through a powerful data layer
Agile
Authoring
Tailored
Instruc%on
Data
Layer
L
R
L
R
L
R
L
R
L
R
P
P
?
Micro
Learning
Objec%ve
L
R
L
R
L
R
L
R
P
PP
Personalized
Learning
22

23.
Key
benefits
to
all
stakeholders
Discussion agenda
23

24. Adaptive learning unlocks organizational performance
Measurement
Mastery
Efficiency
Engagement
Agility
24

25. Managers & Trainers
25
25
• Managers
can
use
the
learning
data
to
beeer
understand
organiza'onal
readiness
and
learning
impact
• Trainers
have
the
ability
to
op'mize
training
resources
and
tailor
training

26. Measurement
26

27. Measurement
27

28. Learners take ownership of their learning
RECHARGE
25
%
35
%
30
%
10
%
28
• Learners
gain
mastery,
with
confidence,
learning
at
their
own
pace
• Personalized
real-­‐'me
adap've
keeps
the
learner
engaged
• Learners
gain
mastery
in
the
most
efficient
manner

29. Authors and subject matter experts can continually
refine content
Learners
can
offer
feedback
70%
29
• Authors
see
real-­‐'me
data
on
the
effec'veness
of
content
and
ques'ons
• Access
to
'me
spent
on
learning
resources
• Probes
performance
on
associated
learning
resources
• Learner
answers
changing

30. Adaptive learning unlocks organizational performance
Measurement
Granular
data
to
measure
learning
impact,
iden'fy
poten'al
areas
of
risk
and
gauge
organiza'onal
readiness.
Mastery
Learners
complete
their
learning
confident
that
they
have
mastered
the
content.
Efficiency
&
Personaliza%on
Learners
proceed
at
their
own
pace
and
preferred
modality
of
learning,
while
real-­‐'me
personaliza'on
accounts
for
varying
incoming
skill
sets
&
exper'se.
Engagement
Real-­‐'me
personalized
learning
paths
and
associated
mastery
leads
to
enhanced
engagement
with
both
the
training
and
the
company.
Agility
Micro-­‐content
assets
and
data
collected
within
the
adap've
data
layer
can
be
leveraged
to
quickly
revise
content
and
make
new
courses.
Mobility
Learners
can
complete
the
training
any'me,
anywhere
30

31.
I
want
it,
how
do
I
sell
it
internally
Discussion agenda
31

32. Case Study: Global Professional Services Firm
5-hour course transitioned to the adaptive learning platform
6,000 Employees - Time to Mastery
Learner (6,000 total learners; long tails of distribution curve are plotted)
Immediate
ROI
32%
FTE/hrs
recovery
32

33. $3,750,000
$1,200,000
6,000
learners
x
$125/
hr
x
5hr
=
cost
of
standard
5-­‐hr
training
Redistributed
Produc'vity
(32%)
6,000
learners
x
$125/hr
x
3.4hr
=
cost
of
self-­‐paced
adap3ve
course
$2,550,000
- =
Although the anticipated time-to-completion for the course was 5 hours, the average
learner finished in 3.5 hours, due to efficiencies provided by the adaptive algorithm,
which focused learners on the areas where they struggled.
ROI: Potential Revenue
Although
the
an'cipated
'me-­‐to-­‐comple'on
for
the
course
was
5
hours,
the
average
learner
finished
in
3.4
hours,
due
to
efficiencies
provided
by
the
adap've
algorithm,
which
focused
learners
on
the
areas
where
they
struggled.
33

34. Case Study: Global Professional Services Firm
5-hour course transitioned to the adaptive learning platform
Flexibility
Time
spend
on
learning
resources
and
associated
learner
performance
provided
ac'onable
data
to
improve
courses.
Modular
content
also
permieed
rapid
update
and
revision
to
learning
materials.
Mindset
Change
No
learners
were
lem
behind.
Each
learner
achieved
mastery
at
his
or
her
own
pace.
All
learners
must
demonstrate
mastery
to
complete
the
program
of
study.
Diverse
Learners
Learners
joining
the
firm
with
significant
previous
experience
(#1)
and
those
with
limited
experience
(#20)
were
each
uniquely
mo'vated
and
challenged
on
their
personalized
path
to
mastery.
Time
&
Cost
Learning
'me
reduced
by
32%
from
original
course.
One
original
5-­‐hour
course,
reduced
to
3.4
hours
when
made
adap've,
across
6,000
learners,
delivered
9,600
hours
of
repurposed
produc'vity
back
to
the
organiza'on.
Impact
Click
by
click
learning
data
at
the
learner
and
cohort
level
provided
rich
data
to
understand
and
act
on
organiza'onal
readiness.
34

35. CURRENT LEARNING CHALLENGES DESIRED ENHANCEMENT
Instructor Lead
4 courses led by an instructor
in a classroom setting.
80 hours of instruction.
Includes simulations & role
play.
Learning Effectiveness
Cohort possesses diverse
experiences and varying
individual learning capacity.
Learning Modality & Cost
Large Millennial population, but
traditional learning style with high
expense.
Impact
Ensure that each learner is
uniquely challenged and
motivated.
Transition from “seat time” to
mastery.
Demonstration
80 hours of cohort & individual
demonstration.
Led by 3 instructors, primarily in
group sessions.
One Size Fits All
Demonstration is not optimized
for cohort, or individual
strengths & weaknesses.
Suboptimal engagement.
Personalized
Ensure that each learner is
ready to perform, confidently in
the most effective & efficient
manner with demonstration
tailored to each learner’s data
profile.
Case Study: Insurance Firm
4 Week Training Program
35

36. Learn
&
Perform
LEARN
Transform
current
4
classroom
courses
sessions
into
adap've
micro-­‐learning
modules
PERFORM
Use
learner
specific
data
to
guide
group
&
“shoulder
to
shoulder”
demonstra'on
sessions
with
micro-­‐learning
remedia'on
Case Study: Insurance Firm
4 Week Training Program
36

37. Week
1
M
T
W
TH
F
Morning
Learners
• C1
Adap've
Learners
/
Trainers
• C1
Classroom
&
Demonstra'on
Learners
/
Trainers
• C2
Classroom
&
Demonstra'on
Learners
• C3
Adap've
Learners
/
Trainers
• C3
Demonstra'on
Agernoon
Learners
• C2
Adap've
Trainer
• Analyze
C1
adap've
data
• Plan
Demonstra'on
Learners
• C1
Remedia'on
Adap've
Trainer
• Analyze
C2
adap've
data
• Plan
Demonstra'on
Learners
• C2
Remedia'on
Adap've
Learner
/
Trainer
• 1-­‐1
mentoring
as
deemed
necessary
Learners
/
Trainer
• C1;
C2
classroom
summary
&
discussion
Trainer
• Analyze
C3
adap've
data
• Plan
Demonstra'on
Learners
• C3
Remedia'on
Adap've
Learner
/
Trainer
• 1-­‐1
mentoring
as
deemed
necessary
Adap've
Learning
Classroom
Demonstra'on
Mentoring
37
Case Study: Insurance Firm
Classroom training reduced by 80%; Demonstration & Mentoring reduced by 30%.

38. M
T
W
TH
F
Morning
Learners
/
Trainers
• Classroom
summary
&
discussion
of
Week
Learners
• C4
Adap've
Learners
/
Trainers
• C4
Classroom
&
Demonstra'on
Learners
• C4
Remedia'on
Adap've
Learners
/
Trainer
• C4
classroom
summary
&
discussion
Agernoon
Learner
/
Trainer
• 1-­‐1
mentoring
as
deemed
necessary
for
Week
1
Modules
Learner
/
Trainer
• 1-­‐1
mentoring
as
deemed
necessary
for
Week
1
Modules
Trainer
• Review
C4
adap've
data
• Plan
Demonstra'on
Learners
/
Trainers
• C4
Demonstra'on
Learner
/
Trainer
• C4
1-­‐1
mentoring
as
deemed
necessary
Learner
/
Trainer
• C4
1-­‐1
mentoring
as
deemed
necessary
38
Case Study: Insurance Firm
Classroom training reduced by 80%; Demonstration & Mentoring reduced by 30%.
Week
1
Adap've
Learning
Classroom
Demonstra'on
Mentoring

39. Variable
Amount
#
of
Courses
FTE
Training
Cost
FTE
Adap%ve
FTE
Cost
Savings
Learners
1,000
→
1
$53,280
$36,230
$17,050
FTE
Hourly
Rate*
$53.28
5
$266,400
$181,152
$85,248
Avg.
Time
Saved
per
Course-­‐Hour
32%
10
$532,800
$362,304
$170,496
20
$1,065,600
$724,608
$340,992
Variable
Amount
#
of
Courses
FTE
Training
Cost
FTE
Adap%ve
FTE
Cost
Savings
Learners
50,000
→
1
$2,664,000
$1,811,520
$852,480
FTE
Hourly
Rate*
$53.28
5
$13,320,000
$9,057,600
$4,262,400
Avg.
Time
Saved
per
Course-­‐Hour
32%
10
$26,640,000
$18,115,200
$8,524,800
20
$53,280,000
$36,230,400
$17,049,600
ROI: Repurposed Productivity in Operating Expense savings
*
Bureau
of
Labor
Sta3s3cs
avg.
for
“Finance
and
insurance”
workers,
(Sep.
2017).
39

40. MHE
Improvement across all applications
Safety
Sales
Technical
Leadership
Learning
Science
Platforms
40

41. Benefits of Adaptive for the Healthcare Industry
Key
issues
in
healthcare
Key
benefits
of
adap%ve
model
§ Quickly
changing
landscape
§ New
technologies
§ Diverse,
distributed
workforce
§ Pa'ents
rights/advocacy
§ 100%
Mastery
model,
where
proficiency
is
cri'cal
§ Meets
needs
of
geographically
distributed
workforce
§ Flexibility—adapts
to
changing
content
or
needs
41

42. Benefits of Adaptive for the Retail Industry
Key
issues
in
retail
Key
benefits
of
adap%ve
model
§ Disrup'on
of
tradi'onal
model
with
omnichannel
retail
§ Organiza'on
restructuring
§ Layoffs/dissa'sfied
workforce
§ Can
provide
new
skills
for
new
job
func'ons,
as
needed
§ Beeer
engagement
leads
to
boost
in
customer
service
and
brand
§ Meets
learning
needs
of
geographically
distributed
workforce
42

43. Benefits of Adaptive for the Hospitality Industry
Key
issues
in
hospitality
Key
benefits
of
adap%ve
model
§ Need
for
brand
consistency
despite
geographic
dispersion
§ High
turnover
requires
frequent
onboarding
of
new
employees
§ Rapid
skilling
of
new
workers
sa'sfies
demand
§ Engagement
leads
to
improved
customer
service
§ Consistent
training
ensures
brand
promise
43
43

44. Benefits of Adaptive for the Professional Industry
Key
issues
in
professional
Key
benefits
of
adap%ve
model
§ High
hourly
rate
of
financial,
legal,
and
consul'ng
services
necessitates
efficient
use
of
'me
§ Personalized
paths
translate
into
direct
opex
savings
§ Incoming
professionals
from
a
range
of
backgrounds
(liberal
arts)
omen
require
diverse
approaches
§ Disrup'on
and
pace
of
industry
evolu'on
require
rapid
knowledge
transfer
and
upskilling
44

45.
Selec'ng
what’s
best
for
you
Discussion agenda
45

46. Learner Experience
“Learner
Experience”
Alribute
Descrip%on
1. Adap'vity
scope
How
is
adap'vity
defined?
Pre-­‐determined
branching
or
real-­‐'me
personaliza'on?
2. Lenses
of
adap'vity
How
many
factors
contribute
to
personaliza'on?
3. Learning
science
What
theories
and
research-­‐backed
ideas
are
incorporated?
4. Assessment
level
Are
assessments
just
mul'ple
choice?
Are
there
games?
Ac'vi'es?
5. Metacogni've
Is
confidence
a
component
of
the
experience
in
addi'on
to
accuracy?
46

47. “Authoring
Experience”
Alribute
Descrip%on
1. Bulk
upload,
content
inges'on
Is
it
possible
to
bulk
upload
content
and
organize
it
quickly?
2. Content
effec'veness
data
Does
the
interface
support
content
effec'veness
data
down
to
the
granular
level
of
the
learning
object?
3. Assessment
types
How
may
ques'on
types
are
supported?
What
levels
of
Bloom’s
taxonomy
are
reached?
4. Content
cura'on
How
easy
/
difficult
to
manage
content?
5. Subject
range
Is
the
plaVorm
content
agnos'c?
Can
it
truly
support
both
“hard”and
“som”
subjects?
6. Instruc'onal
design
advice
Are
instruc'onal
design
best
prac'ces
and
'ps
surfaced
readily
in
product?
7. Ease-­‐of-­‐use
How
intui've
is
the
interface?
How
much
training
is
required?
47
Authoring Experience

48. Business
Opera%ons
and
Service
Alribute
Descrip%on
1. Enterprise
grade
security
Do
security
standards
meet
requirements?
2. Proven
efficacy
Are
measurable
results
available?
3. Speed
How
quickly
can
the
plaVorm
make
content
adap've?
4. Scalability
How
scalable
is
the
authoring?
5. Integra'on
Can
you
integrate
the
plaVorm
with
any
LMS?
6. Service
and
maintenance
Is
service
holis'c
and
consulta've?
Does
it
include
all
steps
from
prototyping
to
training
and
support?
7. Pricing
Is
pricing
flexible?
Does
it
work
for
organiza'ons
of
different
sizes?
8. PlaVorm
localiza'on
Can
you
author
in
mul'ple
languages?
48
Business operations and services

49. MHE adaptive leverages investment in science
and technology
$175M+ per Year
in Digital
Platforms
2012
DPG
Formed
2017
Organic
investment
450
Engineers,
etc
200
Enginee
rs, etc
$
1,600+
Adap've
products
4,000
Authors
trained
to
use
MHE
Adap've
5,000,000
Learners
using
MHE
Adap've
10,000,000,000
Data
Layer
Interac'ons
MHE
Adap%ve
Acquisi%on
User
Knowledge
49

50. Poll Results
50

51. We exist to unlock the full potential of every learnerQuestions?
Write to me:
Geoff.Broderick@mheducation.com
51

52. #CLOwebinar
Register for the next webinar!
Surviving the New Skills Economy
Tuesday, May 15th, 2018
Webinars start at 2 p.m. Eastern / 11 a.m. Pacific
Register for all upcoming Chief Learning Officer
Webinars at clomedia.com/webinars