data of learning

www.valamis.com

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DATA OF LEARNINGA 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

E�ective 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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Download from www.valamis.com/data-workshop

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.

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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

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Goal

Example 1: Learning Data for Content Creators

LoganInstructional 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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Goal

Example 2: Learning Data for L&D

AliceL&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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Goal

Example 3: Learning Data for Team Lead

ArnoldTeam 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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Goal

Example 4: Learning Data for Managers

LaraDirector 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 whose mentoring and support played

a valuable part in making this workbook as good as possible.

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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