The document discusses getting started with learning content analytics. It provides an overview of key concepts including why organizations measure learning activities, common data sources, and how to share insights. Examples are presented from clients like Western Union who use analytics to understand learner engagement, reactions, competence and perceptions. The session aims to help participants think more like data scientists and use analytics to improve learning programs and outcomes.
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Outcomes
IMPACT of learning
on business or HR
goals
Effectiveness
QUALITY – Reaction,
Learning, Application,
Impact, ROI, Manager
Support
Building Block #1: Knowing WHAT to measure….
Efficiency
QUANTITY of training
delivered
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Building Block #2: Knowing WHAT data sources
you have
Internal
Primary
SCORM | Kineo Analytics | xAPI | Surveys | Focus Groups |
Knowledge Checks & Assessments
Secondary
Interviews | Collaboration Tools | Performance & Skills
Reviews | Peer Feedback | Sales Results | KPIs | Company
Portfolio
External
Primary
Industry Trends | Accessibility & DEI Reviews | Localizations &
Translations
Secondary
Internet of Things | Social Media | Google Analytics |
Clickstream Data | Mobile Apps
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Learning
leader
Business
& policy owner
Learning
designer
Learner
Who’s your audience? What stories do they need to hear?
Show me whether we’re
creating the best learning
experiences for our employees
and whether we’re adding
value to the business.
Show me that people aren’t
spending unnecessary time on
training and that our people
have the skills we need them
to have.
Show me how
people are
interacting with the
experiences I design
and where I can
improve them.
Show me if we are mitigating
risks and can prove this to
compliance regulators.
Show me that you’re listening
to me and developing
programs that support my
development and give me the
best tools to do my job.
16. Telling an impactful story
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It’s about people, not numbers
17. Telling an impactful story
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Make it visual!
18. Take your audience
on a journey
Telling an impactful story
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19. Telling an impactful story
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Give it real life, relatable context
20. Telling an impactful story
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Describe how the data has
inspired change – share
what you’re going to DO
differently as a result
21. Your thoughts
21
What’s holding your
organization back from doing
more?
What are the biggest barriers?
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analytics
22. What’s holding organizations back?
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Knowing
what data to
collect –
within data
regulations
Cost of
analytics
solutions
Internal
expertise in
analyzing
data
Presenting
data in a
compelling
way
Identifying the
potential
Limits to
SCORM
24. Learning content analytics aren’t
just about who launched what and
who’s completed what – they’re
about understanding how learners
interact with the content and how
they experience the learning.
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26. Don’t just use data as another way
to tick the box – use it to make
better experiences for learners
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Traditional reporting (SCORM)
• Individual Learner details
• Completion & Objective Status
• Time & Score
Learning content analytics
Who did what? How and why?
• Require further/skills processing for
deeper insight
• Aggregated trends on learning
behavior
• Learner Engagement & Performance
throughout the experience
Wider insight via automated visualisation
Limited insight via manual processing
• Interactive dashboards on trends
• Changes in perceptions, confidence,
reactions over time
• Measuring against learning
objectives
30. It’s easy to “drown in data” if we
don’t sort it and categorize it in a
logical way…
…so Kineo uses four dimensions
to categorize data and insights.
….and sometimes we add a few
more….
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32. Engagement Reaction
Competence
(Applied Knowledge)
Perception
(Confidence)
Measures
Ability to apply
knowledge in realistic
scenarios and/or
exercises
Perceived confidence
around an area of
behavior or knowledge
Example
• The difference
between activities
at the start and end
of the learning
• Performance
against different
learning objectives
• Confidence before
and after the
learning
• General confidence
about specific skills
or knowledge
• How they rate
current mastery
level
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analytics
33. Engagement Reaction
Competence
(Applied Knowledge)
Perception
(Confidence)
Measures
Ability to apply
knowledge in realistic
scenarios and/or
exercises
Perceived confidence
around an area of
behavior or knowledge
How someone
interacts with the
learning
Example
• The difference
between activities
at the start and end
of the learning
• Performance
against different
learning objectives
• Confidence before
and after the
learning
• General confidence
about specific skills
or knowledge
• How they rate
current mastery
level
• How someone
uses the learning
• Time spent /what
content visited:
• Duration
• Repeat visits
• Completion
rates
• Accessibility
• Usability
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started
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content
analytics
34. Engagement Reaction
Competence
(Applied Knowledge)
Perception
(Confidence)
Measures
Ability to apply
knowledge in realistic
scenarios and/or
exercises
Perceived confidence
around an area of
behavior or knowledge
How someone
interacts with the
learning
People’s opinion or
feelings about the
course and intent to
apply
Example
• The difference
between activities
at the start and end
of the learning
• Performance
against different
learning objectives
• Confidence before
and after the
learning
• General confidence
about specific skills
or knowledge
• How they rate
current mastery
level
• How someone
uses the learning
• Time spent /what
content visited:
• Duration
• Repeat visits
• Completion
rates
• Accessibility
• Usability
• How do they feel
about the course?
• Was it useful and
relevant?
• Did they enjoy it?
• Intent to behave
differently?
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Meet Heather: She oversees a large mandatory training
portfolio at a global bank
Use Case: Maximize impact while minimizing seat time
• HR department wants
required training to be
delivered as concisely
as possible
BUSINESS NEED
• Know what to do and how
to behave in a range of
situations
• Confidence to apply on
the job
• But - a little fatigued from
years of mandatory
learning
LEARNER NEED
• Focus future training
on what learners need
most
• Reduce seat time
while making sure all
training gaps are filled
DESIRED OUTCOME
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Meet Heather: She oversees a large mandatory training
portfolio at a global bank
Use Case: Maximize impact while minimizing seat time
Engagement
• Average time to
complete?
• Where spending
the most time?
• Where breezing
through?
Reaction
• Did learners
find the course
relevant and
enjoyable?
• Do they intend
to behave
differently?
Competence
• Pre & post
tests – what do
learners
already know?
• What do they
get wrong?
(Applied Knowledge)
Perception
• Confidence
levels going
into course and
after?
• Does
confidence
match
competence?
(Confidence)
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Meet Mo: They are launching an optional course for a global
sales team
Use Case: Building learning programs to inspire culture
change
• Create a global sales
language to enable
consistent internal
and external
customer experiences
BUSINESS NEED
• Use the same terminology
and processes as peers
• Move on from some siloed
behaviours
• Need to squeeze training
in between face-to-face
meetings
LEARNER NEED
• A pilot phase to
inform a full roll-out
• Include feedback from
regional leads on how
well the learning is
being applied in real
life
DESIRED OUTCOME
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Meet Mo: They are launching an optional course for a global
sales team
Use Case: Building learning programs to inspire culture chang
Engagement
• How long are
they spending
on the training?
• Is it in one
sitting or in
pieces?
• What devices
are they using?
Reaction
• Did learners find
the course
relevant and
enjoyable?
• Do they intend
to behave
differently?
Competence
• Using scenarios
or simulations,
can learners
apply the
preferred
behaviors?
(Applied Knowledge)
Perception
• How do they
feel about new
behaviors?
• How effective
do they believe
new behaviors
will be?
(Confidence)
51. Year 1 – Build your strategy & find your stories
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Frame the
Problem
Collect
Raw Data
Clean
the Data
Explore
the Data
Analyze
the Data
Tell Your
Hidden Stories
2
1 3 4 5 6
52. Year 1 – Frame the problem
(what to measure and why, how to collect the data)
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• The goal is to attempt to produce questions
that we feel data might help us find answers
to.
• Build your requirements to the purpose you
are using it for.
• You want to collect purposeful data, not fluff.
• Avoid talking about visualizations, graphs, &
what data to collect.
• Document!
53. Year 1 – Frame the problem
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• Does our expected course duration align with
actual seat time?
• How are learners performing on knowledge
checks & scenarios?
• Are the videos we are producing worth the
time & effort spent?
• How much value does our UI offer?
• 8% of learners reported connectivity issues
previously. How can we help the Learning
Support Team?
54. Year 1 – Collect raw data
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Tying questions to course events and assigning
xAPI verbs & context.
• Track active course time
• Track all video interactions
• Gather detailed question answer & scoring
data
• Track all UI interactions
• Implement connection test in course.
Measure the frequency and types of data loss
issues occur between course, LMS & LRS
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The big project
• 2-hour, organizational-wide compliance
• 4 organizations working collaboratively
• Video-driven episodic experience
• 200,000+ learners
• Cornerstone, Learning Locker, & Adapt
Framework
• WCAG 2.0 AA
• 5 additional languages
• By design, learners are afforded the
opportunity to test out of a final
assessment
• Videos are optional to watch
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56. Year 2 – Analysis at-a-glance
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57. Year 2 – Explore, Analyze & Tell a Story
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• Don’t give learners such a hard time.
• Practice behavior change over strict
competency - allow learner to ask for help
• No change to video approach as they were
well-received
• Implement inactive learner notification
• Slightly raise visibility of course resources
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A cloud-based
analytics service
Works with any LMS
with no need to invest
in an extra platform.
Included with our
Adapt builds
Get more value from
the content you
develop with us, as
standard.
Goes beyond
SCORM
Track metrics, trends
and patterns that
normally require an
analytics platform.
Advanced
dashboard
visualizations
Identify trends within
your data through
interactive
visualisations, with
preset views.
Kineo Analytics service overview
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Kineo Analytics – Better
Data for Better Decisions
▪ Cloud service captures
anonymized data about
usage of Adapt learning
▪ Goes beyond SCORM
tracks across several data
dimensions for deep insight
▪ Works on any LMS without
custom set-up and at no
extra cost
Standard
SCORM
Kineo
Analytics
Individual Completion Status x
Individual Assessment Score x
Question Responses x
Average Duration x
Average Attempts x
Perception/Confidence Changes x
Pre versus Post Assessment Scores x
Percentage Incorrect Responses Per Question x
Number of Attempts Per Question x
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Kineo Analytics
Data on how audiences have used your
solutions:
• Number of users / launches / completions
• Average time to complete
• Pre and post assessment scores
• Impact on knowledge, perceptions and
confidence
• Learner experiences
• Different devices used to access learning
Fully
GDPR
compliant
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Cammy Bean
Senior Solutions Consultant
Cammy.bean@kineo.com
https://www.linkedin.com/in/
cammybean/
Joe Replin
Senior Technical Consultant
Joe.replin@kineo.com
https://www.linkedin.com/in/
joseph-replin/
Stay in touch!
Rodrigo Bolanos
CEO, Kineo LATAM
Rodrigo.bolanos@kineo.com
https://www.linkedin.com/in/
rodrigobolanos/
www.kineo.com
Find out more about Kineo Analytics
Get in touch: info@kineo.com
Keep up with us on socials:
https://www.linkedin.com/company/kineo