Performance Matters - Learning and Performance - 29

MICROLEARNING
& NUDGES
D A TA SCIENCE
& EVALUATION
GAMIFICATION
As experience designers, we know that
one big mindset shift is considering
how we might improve our methods of
tracking progress. Gamification comes
to mind here, but there's a risk of
oversimplification when it simply focuses
on elements like leaderboards and
badges. Thinking more broadly about
game mechanics gives us the power
to make meaningful progress visible in
ways that are translatable to our work.
Whether through leaderboards, points,
experience, levels, etc., game designers
place emphasis on visualizing progress
and providing feedback to the player. All
of this contributes to creating a state
of flow where difficulty and ability are in
balance. As we go forward, we should
continue to look for ways to do the same.
Some questions for your consideration:
How can we create feedback loops that
inform our performers so they can be
empowered in their own journeys of
development? How can we make their
growth and development visible? What do
our technology platforms allow today,
how might we customize them to go
further, and what should we plan to
implement tomorrow?
" Microlearning is dead " sounds radical,
but its demise really just marks the
transition to nano, pico, and beyond!
In the past, it made sense to go from
the 45-60-minute eLearning course
and challenge ourselves to do it in 15
minutes. Yet, today's research shows
that a 3-minute video might have us
bored and skipping forward, which means
the microlearning of old isn't micro
enough. What might be the answer?
Nudges. Research shows that as much
as 40% of adult behavior is habit and
routine.2
As we design experiences,
instead of always starting from a blank
canvas and trying to " train " employees
through repeated memorization, we
have several new opportunities. We
can support them through the creation
of new habits (breaking old or building
new), we can reinforce desired habits
(through effective nudges), and we can
help change the context of their work
(through new cues or ways of working).
As experience designers, we can learn
from nudge units around the world (or
from our favorite online retailers) by
thinking about meaningful reminders that
support remembering (it is still in the cart
unpurchased!); showing performers how
they compare to their peer group ( " 65%
of those around you complete their taxes
on time " ); or by showing the pro-social
(or pro-company); impact that individual
behaviors can have ( " your efforts enable
us to build a better world " ).
2 Duke University, Neuron, 2016.
LEARNING
EXPERIENCE
PLATFORMS
Traditional LMS platforms are more
focused on prescription and compliance-
which still meet important business
needs. However, the reality for modern
learners in dynamic workplaces is that
they have less time for formal learning
and need more access to critical
knowledge and expertise at the point
of need. In response, we're seeing new
classes of learning platforms such as
the Learning Experience Platform (LXP)
and the Integrated Learning System
(ILS) that put the learner at the center
of the learning experience (as opposed
to the content). These modern learning
platforms harness the powers of artificial
intelligence (AI) and machine learning
(ML) to drive relevant and personalized
learning. They also leverage performance
science activators like nudges and
triggers, along with peer connections and
communities, to keep learners engaged
daily. Furthermore, they readily integrate
with other applications in the talent
ecosystem, enabling organizations to
create a culture of continuous learning
that encourages employees to own
and self-direct development, while
also supporting defined talent
management strategies.
Data science comprises a variety of
tools and capabilities: computation and
reporting, algorithms, process simulation,
statistical analysis, machine learning,
and more. Commerce, business, and
communication are all built on data.
Learning will not be untouched. Great
promise awaits our ability to marshal
our learning and performance data. In
recent years, working behind labels like
learning analytics or learning engineering,
many people are thinking deeply about
how to use data to support learning. This
will be tricky, however, and we will make
mistakes. We may accidentally measure
what's easy, not what's important.
We may make decisions based on
meaningless data. We may waste time
chasing unicorns. Certainly we can and
will find useful data today, but we must
be careful in our work. We all want to
shepherd our organizations into the
future of big data, learning algorithms,
data visualization, predictive analytics,
etc. However, we have to ensure that
our data is relevant and meaningful. As
we continue elevating how we measure
learning and performance experiences,
we recommend beginning with a
broad learning and training evaluation
measurement (LTEM) framework, and
then digging into specific improvements
to your surveys. From that solid
foundation for sound evaluation and
clean data, you'll be able to move
forward with collecting, visualizing,
interpreting, and leveraging that data
in new and imaginative ways.
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PERFORMANCE MATTERS / PG 29
Performance Matters - Learning and Performance

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