e Learning-data-of-learning need analysis

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DATA OF LEARNING
A workbook for L&D to help use learning
data to accelerate change
WORKBOOK

Data of Learning www.valamis.com
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Foreword
Data is the hot topic in L&D right now, as proven by Donald Taylor in The 2019 L&D Global
Sentiment Survey.1
And it should be, since it is packed with answers to the kinds of questions
we don’t yet even know how to ask.
The world is changing at an accelerating pace. Companies should help their most important
asset—their employees—to learn the skills needed in this changing work environment. And
here, the data is invaluable. Data’s value for L&D can be found, for example, in making better,
more personalized recommendations, in design, and above all, in measuring learning’s impact
on business.2
The data helps learners avoid unnecessary learning by showing them precisely
what they need to learn to complete a task. Data can help L&D make more efficient learning
content, so that people spend less time learning, leaving more time for productive work.
Learning Analysts at Towards Maturity found out in their study about L&D’s relationship with
data that “51% [of L&D professionals] say they cannot use data effectively due to L&D lacking
in-house data skills.” The same report states that the data is primarily collected in the space of
learner activity, and despite L&D investing in technology, the opportunities data brings are not
fully exploited. L&D could use data better to improve both learner performance and experience.2
This paper was written to answer the question: How do we get started with exploring data
about learning?
In our white paper about Learning Impact, we looked into the effect learning can have on
business and gave an analytics framework to help in measuring it. In this workbook, we take a
slightly different approach, and give you a practical tool to use when planning the leveraging
of learning data in your organization.
After the introduction to learning data, you get a workbook with four user profiles that you can
use to find out how learning data can be implemented for each role. The workbook is based
on a popular workshop we have held at learning events and for our customers. You can use
the workbook as a foundation when running a workshop for your team about learning data.
Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
What Is Learning Data?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Why Do You need Learning Data?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
How to Use Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
What Should You Measure?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Workbook: The Data of Learning for Different Roles
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The Workshop Template. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Example 1: Learning Data for Content Creators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Example 2: Learning Data for L&D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Example 3: Learning Data for Team Leads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Example 4: Learning Data for Managers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Where Do I Get the Data?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Resources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Authors
EDITOR: Jenni Härkin LAYOUT & VISUALIZATION: Kimmo Pukkila
SUBJECT-MATTER EXPERTS:
Anna Khokhlova
Anna Khokhlova is an experienced Business Analyst
Consultant working with big data analysis. She has
strong knowledge about learning data gained through
her Master’s studies and work with customers. Anna
combines data sources and visualizes complex
data sets and interdependencies in intuitive and
easy-to-understand dashboards and reports. She
is responsible for creating technical solutions for
analytics in Valamis LXP.
PhD Ana Gebejes
Ana Gebejes is experienced in creating and growing
innovative digital learning programs. She has been
teaching in higher education for nine years and has a
PhD in computer science. At Valamis, Ana is respon-
sible for making the learning impact happen—she
collaborates with clients closely in identifying their
learning and business needs, then translates those
needs into digital learning.

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What Is Learning Data?
In this paper, we define learning data as the data that is collected from an individual’s learning
actions, behavior, and context. However, to make learning analysis and measuring learning’s
impact on business possible, learning data should be brought together with behavioral data,
HR data, performance metrics and business data.
Data can be gathered from various sources. It can be something you gather when talking to
your colleagues and learners. Data that might be useful to you can also come from social
media, personal reflection, focus groups, and case studies. Even data from external sources
such as field analysts can be included in your learning data set.3
Learning activity data can be, for example, what learning materials the learner has used, how
much time they’ve spent in the learning materials, the tests they’ve taken, the competencies they
have, and possibly their learning history. This data can be tracked by a corporation’s learning
system and include for example CVs, as well as data the learner manually adds in the system.
Behavioral data can be collected from the company’s digital communication channels such
as Slack. Activity data from communication channels shows who talks with whom and how
teams communicate, and helps to track the social learning and the changes learning makes
in employees’ behavior.
Data can be clicks, numbers, text, video, audio, messages, emails, or pretty much anything
digital. Lori Niles-Hofmann also lists search terms, views, likes, shares, downloads, and traffic
data as valuable data points4
But why do you need all this information?
Why do you need Learning Data?
What do you need learning data for? A content creator has different uses for data than an
L&D specialist, team lead or management. It all comes down to you: what do you want to
achieve with the learning data? Questions you need to ask from the data depend on your
organization’s needs.
According to Overton, data is at the very core when building a powerful base for making
decisions: to help improve performance, engagement and business impact.5
Learning strategist Niles-Hofmann swears in the name of data-driven learning design (DDLD).
She reasons that learning data is a guide to faster and better learning design decisions. It is
also a useful tool when learning designers need to convince C-level stakeholders to invest in
their learning programs. Data resonates.6
“If you arrive at the stakeholder table prepared
with key data in hand, you have indisputable
evidence to guide better decisions.”
LORI NILES-HOFMANN, LEARNING STRATEGIST4
Learning data can be used for tracking learning activities, finding gaps in learning content, and
locating outdated learning materials. It can be used to show who is learning, how much time
they are spending, and the correlation between these two. It helps you to find out when the
learning is happening. It can also help you to find the most suitable people to work in your team
and to keep their skills up to date. With the help of learning data, you will discover the most useful
learning programs for your business and how learning correlates with your business outcomes.
FIGURE 1 An example of visualized learning data in Valamis LXP.7
Business analytics can be descriptive, diagnostic, predictive and finally prescriptive.8
The same
principle can be applied for learning analytics as well. In more sophisticated learning systems,
data can be used to predict the learning needs and finally give the learners recommendations
of what they should learn next.
When you combine performance data with learning data, you can for example find out the
learning patterns of your best-performing consultants and suggest that all consultants try out
their learning tactics.
Data is a tool, a tool with great possibilities, but only if it is used right.

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How to Use Data
The principle of evidence-based management stands in learning practices as well: ‘’The basic
idea of evidence-based practice is that good-quality decisions should be based on a combi-
nation of critical thinking and the best available evidence.”8
In order to analyze data, you need to define the problem you are trying to solve. What are the
main challenges in your work? Could you use data to solve them? Each company and job role
has its own challenges. You can get suggestions of “what could I do with data” from the outside,
but what you need to measure and analyze is up to you and your company’s processes. As
Overton emphasized in her webinar: the secret ingredient to data success is you, and curiosity
is the key to unlock the possibilities of data.3
Learn to ask the right, value-driving questions. Always ask “why?” and “how will this help me to
solve the challenge I have?” Ask yourself: “What could I do if I could link all the different data
my company collects into learning data?” You can use the worksheet we hand out to you in
this paper to help in diving into the world of data. The question you choose to ask leads to a
hypothesis. In a simple example, if your question is: “Do employees use the learning system?”,
your hypothesis could be, “The employees are not using the learning system.”
Now you are ready to dive into the data and start looking for evidence for your hypothesis.
The aim of these exercises, is to see whether your hypothesis is proven to be right or wrong
with the support of learning data. Looking into reports on “time spent in the learning environ-
ment,” you can see the answer to your question. When taking more variables such as job role
into account, you might find out that certain teams or job roles are more active than others.
Once you have evidence to support or discard your hypothesis, it’s time to plan actions. Do
some teams need more encouragement in using the learning environment than others? Or
maybe you want to ask another question: “Why are people not using the learning environment?”
and dive deeper into the data rabbit hole.
Ultimately, data is a tool for you to prove that something is or is not working as you planned.
Reports are built to visualize the data into an understandable form to support the analyses you
make. At this point, it is important to understand that data should not be used only to prove
that something has happened, but also as a tool to help predict what will happen in the future.
As Tom Ridley reminded us in the webinar with Laura Overton: even though we aim to find
answers to our predefined questions in the data, it is also sometimes important to explore the
data. Pure exploration can help you find something you didn’t even know to look for.3
What Should You Measure?
Based on the questions you have defined, you know what phenomena you want to under-
stand. With that in mind, you need to define what you should measure in order to gain an
understanding of the phenomena you are trying to understand.
FIGURE 2 What are we using data to track? Source: Towards Maturity,
L&D’s Relationship with Data2
Figure 2 by Towards Maturity shows some of the data that companies measure or could measure
when it comes to learning. Most tracked data comes from learner activity and engagement.
The least-measured area is behavior, and only 43 percent of L&D departments make efforts
to measure business KPIs.2
Behavior and social learning are not the easiest things to track—because of their social nature.
However, in the longer run, both are important metrics to follow when it comes to the question,
“Did the learning actually happen?”
With today’s technology, it is possible to give every employee a small device that tracks, for
example, the conversations they have. With the digital communication platforms available,
there’s no need to go to that extreme. With the help of AI, text analysis can be used to deter-
mine the tones of voices used in communication to help in further analyzing the behavior
and shape it with learning.
50% 51% 68% 40% 61% 83% 13% 29% 32% 37% 42% 43% 27% 30% 31%
Dropout rate
Active users
Total hours spent
Opinions of users
Rating
Completion rates
Behaviour
Campaigns
Physical location
Manager feedback
Budget & Headcount
Business KPI‘s
Sector/Industry
data
Effective practice
benchmark
Learner data
ACTIVITY ENGAGEMENT EXPERIENCE PERFORMANCE WIDER CONTENT
PROBLEM HYPOTHESIS DATA ANALYSIS ACTION

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Data of Learning
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Whatever metrics you choose to track, make sure that the data is of good quality and comes
from a trusted, valid source. Be sure to measure the correct things. For example, if your goal is
to improve engagement, it is not enough if you only measure completion and dropout rates.
The Data of Learning for Different Roles
Use this workbook to find out what kind of learning data is useful for you. On the next page,
you will see a template to help you in your attempt. In the following four examples you will
find four options to use the template.
In this paper, the roles we go through are Instructional Designer, L&D Specialist, Team Lead
and Director of L&D. In the examples, learning paths means a series of learning actions taken
in order to achieve learning goals. After you are done with the worksheet, choose one ques-
tion, form a hypothesis, and test it according to the instructions from the “How to use data”
section of this workbook.
Instructions to use the worksheet:
Choose a name and role for the persona that you want to explore.
Define the goals for the persona.
Write down the key challenges the persona has in their work.
Define questions the persona asks when facing the challenges in their work.
Try to come up with questions which answers can be measured.
Think how the answers to the defined questions would help in finding/
measuring the impact learning has on business?
Think about what data the persona needs in order to answer their questions
and where to collect that data.
What reports and analytics does the persona need in order to make the data
dable?
Define some possible actions based on the data analysis.
WORKBOOK
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2
3
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5
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The
workshop
template
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NAME
ROLE
2
3
4
5
6
7
8
GOAL
KEY
CHALLENGES
OPEN
QUESTIONS
(PICK
FEW)
DATA
NEEDED
AND
HOW
TO
COLLECT
IT
REPORTS
NEEDED
HOW
THIS
HELPS
TO
MEASURE
THE
LEARNING
IMPACT
POSSIBLE
ACTIONS
AFTER
ANALYSIS

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1 Name & role Key challenges Open questions (pick few)
How this helps to measure the
learning impact
Data needed Reports needed Possible actions after analysis
2
3 4
5 6 7 8
Goal
Example 1: Learning Data for Content Creators
Logan
Instructional designer
To create quality learning content
that helps learners to learn
efficiently.
Is the learning linking to the
business needs?
Keeping the learners engaged and
motivated
Proving that learners are studying
the materials
Knowing who is not studying, and
how to engage the unmotivated
learners
Learning activity report
Pass/fail report
Outcomes—did learning happen?
Engagement report
Delete old content
Find weak parts of the content and
improve it
Change learning path for learning
modules
Change learning module formats
Learning activity data: time spent
in learning, time spent in specific
learning materials and tests, what
learners do in learning materials,
where in the tests they tend to
answer wrong, ratings on learning
materials, learning path data
Feedback from learners, happy/not
Whenever we see there’s
someone failing, we want to see
why—why is the learner failing?
I can see which learning topics are
linked to learning/satisfaction and
therefore to business goals
Does the learning have an effect on
performance?
I can cut the time wasters and
non-working pieces from the
learning materials
I can spot early signs that learning
is not working (iterative approach to
improvement).
Can I improve the learning
program?
Do employees enjoy the learning
program?
What paths do users choose in their
learning?
Why can’t users complete a
module?
What are the weakest points in the
learning program?
Is the content helping to achieve the
set learning goals?
Is the content motivating the
learners?
Is the content relevant to the
learners and their tasks?
Is the learning delivered in a format/
pieces that people can learn from?
Is the learning in the correct
context?
What is the best time to publish
training?

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learning impact
2
3 4
5 6 7 8
Goal
Example 2: Learning Data for L&D
Alice
L&D Specialist
Build learning programs that drive
change in skills and behavior,
support content creators, and
support learners.
Identify the need for learning
and plan how the learning will be
implemented.
Keeping the workforce competent
Improving the learning to support
the business better
Seeing that the learning plan is
changing behavior
Decreasing the time used in learning
Skill reports
Platform reports
Turnover/costs reports
Employee retention
Time spent learning (cost)
Time to competence
Learning trajectory/trends
Launch a marketing campaign
Shorten employee onboarding
time/time spent in learning
Identify pain points of the programs
and improve them
Change the content/learning plan to
fit the trends/formats that learners
prefer
Trainings completed
Pre-survey and after-survey data
Success/failure
What learning material is pulling
What learning programs are
successful
What format of learning works
Data on the LMS performance (Does
it work? Do people like using it?)
HR data
Performance and behavioral data
It helps me to understand the
cost of learning, and the time it
takes to experience an increase
in competency or modification in
behavior. Is the learning serving
business?
How much time do users spend on
learning?
What can I do faster and better?
Are there correlations between
learning and time spent?
Who are the top learners?
Which users are active, and which
are not? Why?
Who needs personal intervention in
the learning process?
Do your learning programs have a
real effect on how the employees
do their jobs?
Which channels are used for
learning?
What times of the day do people
learn during?

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learning impact
2
3 4
5 6 7 8
Goal
Example 3: Learning Data for Team Lead
Arnold
Team Lead
Build and lead competent, high-
performing teams and make sure
that team skills develop within the
project needs in order to deliver
successful projects.
Keeping the team competent
Delivering projects successfully and
on time
Finding skilled team members
Supporting team members’ career
development
Motivating the team to keep their
competencies up to date
No time to spare
Individual-level analysis
Suggested learning materials
(prescriptive)
Learner Type profiling
Team performance
Feedback on the usefulness of the
offered learning materials
Change role for employee
Create more competent teams
Motivate/support employees to start
or continue learning
Skill reports/mandatory compliance
certificates?
Time spent learning (cost)
Time to competence
Learning trends
Work satisfaction
Data on team performance/
behavior
Find potential problems in learning
I can see how the changes in skills
affect the productivity in my project.
Which skills did his team members
get/which skills do they have?
Where are the skill gaps, and how
can I prevent them from happening?
Are there weak points in the skills of
my team?
What is the level of competence of
the employees?
Are my team members engaging
with the program?
Which employee is best suited for
which role?
How can I help my team to learn
needed skills?

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learning impact
2
3 4
5 6 7 8
Goal
Example 4: Learning Data for Managers
Lara
Director of L&D
Drive better business performance
with learning and retain skillful
employees.
Proving to the board that learning is
affecting business results positively
(check LinkedIn slide from the
presentation)
Keeping up with all the changes in
needed skill sets in the company
Retaining existing, good employees
by retraining them instead of
firing-hiring
Change in sales caused by learning
Change in performance indicators
caused by learning
Develop strategy or define new
strategy
Focus on programs which have
more impact on the business
Invest more in L&D
Performance data
Business data (KPI)
Learning data (location, teams,
hours spent, completions)
Behavioral data
HR data
Skills data
I know better which learning
programs support the business
I know better where in learning to
invest
I can reason more effectively to the
board
Do employees use the learning
system?
What program has the most impact?
Does learning affect the business
outcome?
Do my learning programs have a
real effect on how the employees
do their jobs?
How does learning support
company strategy and company
goals/values?
How do I improve profitability with
learning?
Are there any user/employee trends?
How do I improve employee
retention?
How do I make sure that people
are engaged in the on-boarding
process?

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Where Do I Get the Data?
Your existing learning environment may already be collecting learning data. If not, it’s important
to get started.
There are a few ways that data can be collected within the learning environment. In general,
learning experience platforms (LXP) have better range for collecting data compared to tradi-
tional LMS’s. An LXP can also collect data from outside sources integrated into it, including
services like MOOCs, YouTube, and Vimeo.
The most widely used eLearning standard in recording the learning data is SCORM10
. However,
SCORM can’t fully record the always-evolving eLearning. A more advanced way to collect data
is to use the Experience API (xAPI) standard. It is a standard that allows the tracking, storing,
and sharing of the learning experiences of the user across platforms and in multiple contexts,
including offline experiences. However the data is collected, it ends up saved in the Learning
Record Store (LRS).
Besides your learning management system, useful data is hiding for example in your employ-
ee’s CVs, emails, CRM system, communication platforms, recorded calls, customer support,
surveys, and the list goes on. With artificial intelligence in the picture, data no longer needs to
be in numbers. AI can analyze text, video, audio, and images as well.
Use technology to harness the power
of your learning data
Integrations make it possible to blend together more comprehensive and thorough learning
paths, but in many cases make it more difficult to track the learning process. This is why it is
important that the learning ecosystem is built to work together with different systems and
data sources. This is also crucial when you want to make connections between learning and
business performance.
Think about all the internal systems you have that you use for storing information. You may
have something like OneDrive, Vimeo, Dropbox, or another platform of this type.
The search for learning materials goes beyond your own systems. Where do you go when you
need to learn something in a bit more structured way? This could mean LinkedIn Learning, edX,
or some other MOOC. YouTube is a massive store of knowledge as well—will you be able to track
the learning there? How about those VR lessons you are planning to test run in the near future?
When all your preferred learning and knowledge libraries are integrated with your learning
platform, you can access them by using the search in the learning platform and then seamlessly
transferring to explore the documents or learning materials outside the platform. Thanks to
xAPI, the learning actions can be recorded—even those that happen in virtual reality.
Example: Valamis LXP x LinkedIn Learning
Michelle is working as a data scientist at ACME. She starts a cooperation with marketing in a
social media ambassador program for 100 employees to help build thought leadership for the
company. Michelle will do this by sharing her expertise and insights, mainly in LinkedIn, and
interacting with possible leads in conversations. She’s relatively inexperienced with personal
branding and social media.
Michelle’s success is measured in several ways:
LinkedIn Social Selling Index (Start: 20%)
Lead conversions (Starting point: 0)
Personal network growth (Starting point: 280 contacts)
Michelle starts her new task by studying a learning path in company LXP. The learning path
includes the following lessons: Marketing Materials Introduction (internal), Personal Branding
(LinkedIn Learning) and Best Practices in LinkedIn (LinkedinLearning). xAPI sends every action
Michelle takes in the LXP into the learning record store. As she proceeds to the Personal
Branding lesson, her transfer into LinkedIn Learning happens seamlessly from the LXP. xAPI
follows her in LinkedIn Learning, recording all the same activity it recorded inside the LXP.
After the campaign with social media ambassadors has been running for two weeks, ACME’s
Social media manager Tina takes a look at the performance records of her ambassadors.
Michelle’s performance after two weeks:
LinkedIn Social Selling Index: 41%
Lead conversions: 0
Personal network growth: +113
Average ambassador’s performance after two weeks:
LinkedIn Social Selling Index: 58%
Lead conversions: 8
Personal network growth: +92
The performance data shows that while Michelle has succeeded in increasing her social selling
index and growing her personal network, she has gotten zero lead conversions. However,

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on average, ambassadors show higher numbers in social selling and lead conversions. Tina
decides to take a look at the learning records.
The data shows that Michelle did take the internal Marketing Materials lesson, so she should
know about the lead magnets and where to find them. The data reveals that Michelle did not
finish the Best Practices in LinkedIn course. Tina suggests that Michelle revisit the course and
encourages her to ask if something is not clear for her.
AI Takes Your Data to the Next Level
As technology has evolved, AI applications are shifting from concepts into actual operational
algorithms and platforms. These days, AI can actually be useful in the field of corporate learning.
By leveraging all data you have gathered from learning, AI can be used to segment people by
various factors and rules like behavior, attributes, search results, previous knowledge, job roles,
colleagues, etc. Using the data, AI understands the personal needs of the user, what habits
are common, what the forgetting curves are like, and when to act on those indicators. AI can
also be used to curate learning in a personalized and engaging way. It can even predict when
the best time to recommend learning content is.
Corporate learning has a background in education, of course, but with chatbots and AI, it
moves more into the area of behavioral science: How can we influence learning behaviors and
engagement? It is also a good tool in composing organizational change; you can introduce
learning that supports cultural changes and other development needs.
AI has the potential to raise L&D Analytics to the next level, but even when using AI, the same
thing will haunt you: What are the questions you need to be asking?
Conclusions
Data is a useful tool. People from different roles can have different approaches to data, but it
has something to give to everyone. However, data is not a key to enlightenment unless you
take the time to define how it can help you in your work.
GET STARTED – DEFINE YOUR HYPOTHESIS
Define what you want to achieve with data. What are your biggest challenges in your work?
Could data, evidence and analysis help you in succeeding? What would bring the most value
for the business? Have your own workshop to define what each job role could achieve with
data in your company.
COLLECT QUALITY DATA AND BRING ALL DATA TOGETHER
Start collecting data. Get some help from a data scientist to bring together learning, perfor-
mance, and business data in an understandable form and to help you in defining what you
need to measure.
ANALYZE
How does the data collected show a causal relationship or correlation with the variables in
your hypothesis? Can you make actions based on the evidence you have now, or do you need
to ask further questions?
IMPLEMENT CHANGES
Analysis and data is useless unless you take actions based on the insights it provides. Data
can make decision-making faster, and reveal changes necessary for your learning materials,
or the entire program.
REPEAT
Keep curious. As you get familiar with data, you will start having new questions. Your learning
program is in constant change—let data help you make it better.
To learn more about AI and its use cases in corporate
learning, check out our white paper, “Your Definitive
Guide to AI in Corporate Learning.”
www.valamis.com/resources

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Resources
1 Taylor, Donald H. GLOBAL SENTIMENT SURVEY 2019. 2019. https://donaldhtaylor.
co.uk/research_base/global-sentiment-survey-2019/. Accessed 28 Nov. 2019.
2 Ahmetaj, Gent; Overton, Laura. Towards Maturity 2018. L&D’s Relationship with
Data. https://towardsmaturity.org/2018/08/07/lds-relationship-with-data/.
3 Overton, Laura; Ridley, Tom. Webinar: Using Data to Help
L&D Make Better Decissions. 5th Dec 2019.
4 Niles-Hofmann, Lori. Training Industry 2018. Use Data to Super-
Charge Your Performance Consulting. https://trainingindustry.com/
articles/professional-development/use-data-to-super-charge-
your-performance-consulting/ Accessed 15th Jan 2020
5 Overton, Laura. Interview. Conducted by Jenni Härkin, 25 Nov. 2019.
6 Niles-Hofmann, Lori. eBook - Data-Driven Learning Design. http://
www.loriniles.com/ebook. Accessed 24 Oct. 2019.
7 Screen capture of Valamis LXP. 22 Jan. 2020.
8 Center for Evidence-Based Management. Four Types of Business Analytics
to Know | Analytics Insight. 2017. https://www.analyticsinsight.net/four-
types-of-business-analytics-to-know/. Accessed 24 Oct. 2019.
9 “What Is Evidence-Based Management? « Center for Evidence ....” https://www.
cebma.org/faq/evidence-based-management/. Accessed 28 Nov. 2019.
10 SCORM vs the Experience API (xAPI). https://xapi.com/scorm-
vs-the-experience-api-xapi/ Accessed 22 Jan. 2020.
VALAMIS – LEARNING EXPERIENCE PLATFORM
The Complete Learning Ecosystem
Business is changing faster than ever before, and the need for skill development is crucial
for businesses to keep up with the pace of change. There is less time to learn, so learning
needs to be productive, but also engaging. By combining the leading learning technology
with user-centric design, data science, and integration capabilities, Valamis LXP is a complete
learning ecosystem.
STAY COMPETITIVE
Analyze skill levels, proactively upskill your work-
force, and align your L&D initiatives with your busi-
ness goals. Ensure learning is happening just-in-
time and at the point of need.
ENGAGE LEARNERS
Boost engagement with modern, intuitive design,
and flexible learning paths. Add AI-based content
curation, social learning features and certificates
to contextualize and personalize learning content.
Provide consistent online and offline learning expe-
riences for mobile devices.
PROVIDE QUALITY CONTENT
Utilize the built-in Lesson Studio to create your
own lessons, and integrate external course market-
places such as LinkedIn Learning, OpenSesame,
edX, Harvard Business Publishing, and more.
Combine different types of learning contents from
both internal and external sources into learning
paths.
LEARNING THAT IS TRULY PART OF YOUR ECOSYSTEM
Learning should be a part of your employees’
everyday work, not a separate solution. Connect
Valamis with your software systems.
What Can Valamis LXP Do for Your Organization?
Learn more: www.valamis.com
Special thanks to Laura Overton!

Data of Learning
24
This work is licensed under a Creative Commons
Attribution-NonCommercial-NoDerivatives 4.0
International License.
The opinions expressed in this document represent the
current view of the authors on the issues discussed as
of the date of publication. Because of chancing market
conditions, authors cannot guarantee the accuracy of any
information presented after the date of publication. This
white paper is for informational purposes only.
www.valamis.com

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