Traditional learning management systems were good at making sure that employees and managers completed required training, which was usually in person. This is what HR needed, but it didn’t really address the full learning and development needs of the workforce. 

Today, we not only have multiple formal learning modalities, including in-person, virtual instructor-led, video, and self-paced, but we also need to include experiential and social learning. They are crucial for learning new jobs or roles. In addition, we need data about the skills people need and those they have. And we need to provide access to opportunities that let employees apply new skills, practice them, and grow.

Even though we have many amazing ways to deliver learning, choosing learning and career development opportunities that fit the needs of the individual worker has never been more complex. To cut through this complexity, we have to start with the right data and use that data to deliver a personalized learning experience.  

Personalized Learning Starts with the Right Data

Building a personalized learning and career experience for the worker requires a variety of different types of data, including:

• Employment information: job role, organization, work location
• Work experience: years of service or in job, previous employment history
• Performance data: specific metrics, ratings, goal achievements
• Talent profile information: current skills and levels of mastery
• Personal interests: skill and career goals, desire to relocate

But, how do we get the right data and maintain it over time? Basic personal and employment information is maintained in the system of record, but other types of data require more work to find and keep current.

First, the system requires the worker to fill out a talent profile. Most organizations do not do a great job of leveraging all of the data they collected as part of the recruiting process to enrich the talent profile. Even fewer have good ways to keep the data in talent profiles fresh. According to Human Resource Executive, 75% to 80% of workers don’t complete their HR system’s talent profile. 

Many leverage third-party data sources like LinkedIn to make it easier, but the quality of that data can be suspect. And a growing source of insights exists outside of corporate systems altogether, in consumer learning apps like the Pluralsight Skill IQ, digital badges, micro-credentials, and in unstructured data on niche professional networks like GitHub. Today’s systems of record, which were all built primarily to standardize and automate HR processes, can’t possibly keep up.

So what does work? Providing an experience for the worker that not only enables enrichment of the talent profile, but provides value to the worker as they provide that data.

Let’s use the example of Laurie. She’s worked at Acme Corp for a little over a year in a customer service role.

Laurie gets a message asking her if she would do a quick self-assessment of her skills, so Acme can suggest learning and development opportunities for her when she has time.

Laurie answers yes and now she proceeds to answer a question about the five most important skills for her current customer service job. 

Based on the self-assessment, the system suggests personalized learning and development opportunities to help Laurie become better at her job. 

This is a value exchange between Laurie and Acme. Laurie spent time between calls to self-assess her skills, which is valuable to Acme. And Acme provided value to her by suggesting learning and development that can help improve her performance. Using this type of approach to gathering data — asking for information and delivering value back — is critical to starting with the right data.

Using Data for Upskilling to Drive Career Growth

In our example, Laurie’s talent profile was enriched by doing a basic skills assessment. Now the system will use data to provide additional value to Laurie and, in turn, capture more data. In addition to suggesting personalized learning and development opportunities, a modern talent solution should use Laurie’s personal, employment, and skill data to make recommendations on possible future roles by comparing her data to other people in the organization that have similar backgrounds, experiences, and skills. Modern talent solutions use machine learning, or pattern recognition, to provide these kinds of personalized recommendations.

Let’s take it one step further and say that there are three future jobs or roles that are recommended to Laurie. Laurie can drill into more details about the jobs. She can find out more about the roles and responsibilities, the paths that others like her took to get to those future roles, coworkers who have experienced similar transitions and could be good  mentors, and more. The system can also ask another basic question at this point: “Are you interested in any of these future jobs/roles?” If there is an interest, that is data to be collected and used to continue to personalize the experience.

Let’s say that there was a future role Laurie was excited about. The system can continue to do more. For example, the system could ask Laurie to self-assess additional skills to suggest additional learning and development opportunities. This is a great example of upskilling. Those learning and development opportunities for Laurie could be specific training, but they also could be social: become a member of a specific community or subscribe to a specific learning content channel. Or they could be experiential: take on a gig that enables the worker to gain specific skills or work with someone who has gone down a similar path.

That’s a Wrap

This is the last post in our series. We started this series discussing designing for the workforce vs. HR. We laid out some of the fundamental differences between systems that were designed to automate HR processes vs. ones that were designed for the worker first. In the second blog post, we delved into designing for the workforce — if you truly do that, then systems of record are not the center of the universe (or your spaghetti diagram), they are really just another point solution that needs to be considered designing the experience. We also provided an example of a sample persona and journey to illustrate the point. Finally, in this blog post, we went a little bit deeper into using data to personalize the experience. We illustrated the symbiotic relationship between asking for a little data and providing value back before asking for a little more data to provide even more value back.

This is all possible today. Modern talent solutions are designed for the workforce instead of HR. They enable you to better leverage your solution portfolio to deliver better experiences to the workforce. They also leverage data to personalize and add value to the employee while gathering more data. If all your current talent solutions are only capturing data in forms and routing them for approvals online (or in your mobile device), then you are missing out on the art of the possible. Download our full playbook on Aligning HR With Career Experience below!

The post Personalized Learning: The Data Deep Dive appeared first on Degreed.

Maybe your program’s already a priority. You’ve still got a story to tell, one that reinforces the value and impact of learning, talent development, and career mobility. It’s about your company’s highest priorities and biggest goals and demonstrating how an investment in people can help achieve them.

For many organizations, there’s an immediate urgency to consider. The U.S. Securities and Exchange Commission recently mandated human capital disclosure by all publicly traded companies in the United States. This change took effect in November and is expected to eventually affect privately-held companies as well as nonprofits, which means CFOs and CHROs will be asking for metrics that are “material to the business.”

Details Make a Good Narrative Great

The right metrics — data points that take success measurements above and beyond traditional L&D methodologies — can bring your new learning story to life.

In their book, The Expertise Economy, Degreed CLO Kelly Palmer and Co-founder David Blake caution that corporate learning is often viewed as a cost center rather than a revenue-generating function. “Learning typically becomes more reactive than strategic. For example, one business leader asks for a conscious bias training program, another wants an agile development training program, and maybe a third asks for an onboarding program.”

As Palmer and Blake put it, “A strong learning leader who understands business can develop a… strategy based on data from several sources and show how this learning can impact both the business and employees in meaningful ways.” 

Where We’ve Been: Metrics Important to Learning

If you’re like many HR, talent, and learning professionals, you’re looking to be a business enabler who’s more data-driven and strategic about workforce development, so you can guide decisions and investments that deliver results.

Senior executives and CEOs want their teams to lead with advanced analytics, according to Deloitte. In that same study, organizations that reported having the strongest cultural orientation to data-driven insights and decision-making were twice as likely to have reported exceeding business goals in the prior 12 months. Findings from the new Degreed State of Skills 2021 report show that HR professionals rank advanced data analytics among the top ten in-demand skills.

However, traditional learning metrics like course completions and hours of training often aren’t enough to reinforce the value of learning to your CEO, business leaders, managers, individual contributors, or anyone else. Combined with efficiency ratios like hours of learning consumed per worker, the cost of learning per hour, staff-to-worker ratios, and your modality mix, these metrics can begin to tell a basic story of how your workforce interacts with your learning initiatives, but they don’t confirm that anyone actually learned anything.

An employee survey or “happy sheet” might tell you how workers felt about their latest learning experience, help you see how they used what they learned (from their perspectives), and provide some qualitative feedback on how to improve L&D. But this data doesn’t stretch beyond L&D. It isn’t much use to anyone else.

These learning metrics will always be important to track. But as HR and L&D efforts increasingly influence business strategies, advanced companies go further.

Where We’re Going: Metrics Important to Business

Your business has unique needs and goals; they’re the best place to start. 

Perhaps retaining salespeople is a big priority. Maybe it’s reducing the time it takes for engineers to upskill, so they can keep up with new technologies. It might be keeping customer support teams knowledgeable on product updates. These things matter to front-line managers and they matter to business leaders. Because these are the things that impact their ability to drive results.

The new learning metrics are less transactional. They deemphasize time spent learning and focus more on usage through a lens of workforce engagement. They look at social learning and consider content. They address the skills your people have and those they need. And they convey how all of these data points influence the results that matter to your stakeholders, not just your learning, talent, and HR teams.

In our work here at Degreed with hundreds of the world’s most innovative companies, we’ve started to see four new stories take shape. Together, they paint a clearer picture of the changes in culture, behavior, and skills that drive performance.

Your Engagement Story

This is about understanding if your people are participating in learning and feel personally committed to their career development and advancement. With this, you can determine whether you’re creating a culture of innovation and evolution that drives business results. Your engagement story focuses on:

• Activations
• Login frequencies
• Monthly Average Users (MAUs) and whether they’re returning
• Assignments made and completed
• Net promoter score (NPS)

Your Social Story

The goal here is understanding how people at every level of your organization are sharing their expertise, so learning isn’t solely L&D driven. It’s about discovering how your people are interacting to help each other via crowd-sourced, collaborative, peer-to-peer upskilling. It helps you identify who people are turning to for guidance, so you can find, engage, and retain influencers and experts. Your social story looks at:

• Trending or emerging skills
• Recommendations for content, people, and experiences
• Takeaways, including the percentage of workers logging them
• Influencers
  • Subject matter experts (SMEs)
  • Followers
  • Posts
  • Organization network analysis (ONA)

Your Content Story

This is about knowing what your organization is learning. Then you can ask how, where, and what your people want to learn. Your content story considers:

• Popular searches and topics
• In-demand content providers
• Content consumed formally and informally, internally, and externally: courses, videos, podcasts, books, and more
• Content added by employees (social sharing)

Your Skills Story

This tells you what capabilities your organization has currently and those it still needs. These metrics answer the question “Are learning programs helping?” Your skills story addresses:

• Capability gaps
• Emerging skills
• Velocity (how quickly people are learning)
• Skill shift (how quickly individual or organizational skill profiles change)
• Skill value (the dollar value of in-demand skills)
• Skill cost avoidance (recruitment savings from internal upskilling)
• Workforce readiness
• Talent identification
• Career mobility
• Trends

Use Learning Metrics to Stay Agile

Telling your story in new ways is the start of a journey. Your story will evolve as more and more data is collected. What you see at 30 days will be different than what you see at 60 or 90 days, or a year in.

Equally important is developing a culture and processes that use the data you gather — to make changes that continually improve the alignment, efficiency, and impact of your learning investments. 

Your learning metrics story will shift as the needs and goals of your business evolve. Each new chapter provides insights in real time, so you can make better business decisions.

The post The New Learning Metrics: Demonstrating Business Value and Impact appeared first on Degreed.

But true transformation cannot come from one siloed sector of the business — nor can it be achieved simply by purchasing a new tool. For technology investments to have a real impact, HR and L&D need to align with the larger business.

That’s why we asked Amanda Nolen, co-founder of NilesNolen, to speak at our annual LENS conference on how to align HR with the rest of the organization. She opened her presentation with a warning: “If you decide to invest in a new platform, technology alone will not achieve transformation. But it could derail it.”

Learning technology is like a powerful vehicle — a racecar, Nolen said, adding that you need to really understand how to drive it, or you’re going to crash and burn. And if you want to avoid that by driving slowly and cautiously, using the bare minimum of the potential, then what was the point of getting a big, powerful motor vehicle over a basic sedan? 

If you’re going to invest in a powerful tool, you’ll only see max value if you take the time to learn how to use the new technology effectively, and that means changing not only your mindset but the learning culture of your L&D teams as well.

To accomplish this transformation, Nolen laid out six steps:

1.  Refine Your Content Strategy

“You are what you eat,” Nolen said. “And the same is true for an LXP…It’s only as good as the content you feed it.” 

When you think about the types of platforms that people revisit constantly, they are platforms that have fresh, relevant content for consumers every time. Think Instagram, Netflix, even Tinder or Bumble. It’s new, it’s quality, it’s customized. 

So when you think about the implementation of your new learning ecosystem, think past the launch; plan out your long-term content strategy. Nolen recommended a shift from courses to resources. Courses still do (and always will) have their place in corporate learning, but according to recent Degreed research, only 26% of respondents said they went to their HR or formal training teams when they needed to learn something new. The same research showed that:

• 65% of workers use specific websites to learn
• 53% use search engines
• 62% turn to their professional networks
• 45% ask mentors
• 44% ask teammates
• 33% use online social communities

These more accessible and popular learning modalities need to be factored into a modern content strategy.

2. Define a New Target Operating Model

Some learning, like compliance training, happens in an annual cadence or needs to happen at a specific time. But for others, Nolen recommended ditching the traditional course catalog and annual plan. “You’re going to want to free up 60 to 70% of your budget for emerging business needs.” 

In fact, L&D should start acting a lot more like an emergency room. In the ER, a small cut isn’t treated the same as a heart attack. Medical professionals prioritize based on urgent needs, and your L&D department should be no different. 

3. Act Like a Performance Consultant

Acting like a business partner (or an emergency room) is going to mean saying no to things — or at least saying “not now.” To see real results, be brutally efficient about what you build.

Modern L&D departments can no longer simply take orders. Instead, Nolen said, prioritization should be based on data and proven gaps. When it is, L&D leaders become performance consultants to the business, driving real, demonstrable results.

4. Use a Data-Driven Learning Design

To prioritize ruthlessly, L&D teams need good data. “Without data, it’s just an opinion,” Nolan said. “And it’s much harder to push back when stakeholders make a request that you don’t want to honor.” 

Data can also help you make decisions — making you smarter about the learning you offer. Highly impactful L&D leaders use data to help them understand what kind of content works best, and how and when people are more likely to consume that content.

“Don’t wait until your learning initiative is over to find out if it worked,” Nolan cautioned. “That’s an autopsy. You’ve got to measure, measure, measure, and iterate if something went wrong.”

Data is essential to demonstrating impact. Remember to always start with benchmarking the current skills of your organization, Nolen said. And trace your initiatives back to retention, performance, engagement, speed to productivity, and other metrics that impact the larger organization.

5. Manage the Expectations of Your Stakeholders

Everything covered so far is quite different from the ways in which L&D used to traditionally operate. And while there might be internal support initially, stakeholders might become skeptical when they begin seeing the changes or realize their requests won’t always immediately be honored.

It’s extremely important to manage the expectations of all your stakeholders, from your executive teams all the way down to your end-users. Be clear about what’s expected of them, and more importantly, what’s in it for them. 

Tell your advocates and champions what they can do to help market the new learning strategy internally. Rely on your line managers, and remember that real change happens just as often from the bottom up as from the top down.

6. Develop New L&D Skills

There’s been a lot of talk about upskilling and reskilling. They’re not just for the workforce in general. “L&D is going to need a plan for upskilling and reskilling itself,” Nolen said, adding it will take an entirely different skill set than facilitation and instructional design. 

New skills will be required in this new age of L&D. “Skills like marketing, data analytics, digital journalism, community management, and others” are what learning leaders should be looking to develop today.

Unfortunately, not all learning leaders are ready for this transformation. About one-third of the average L&D department will be ready to take on this future of learning and to develop new skills. The next third will be ready to modernize, but it may take some support to get there. The final third might really struggle with the magnitude of this transformation. 

Nolen’s advice for the final, struggling third of an L&D department? Allow them to take responsibility for the parts of learning, like compliance and leadership training, that function in more traditional ways.

How to Choose the Right Solution for Your Organization

If you’re hanging on Amanda Nolen’s every word (like we are) then you’ll love her parting advice. Here’s what she said you need to know before selecting vendors to create your learning ecosystem:

Start with the why. To paraphrase Simon Sinek, one of the Degreed LENS Lite keynote speakers, when you’re searching for a solution ask yourself, “What business goal are you trying to accomplish?” Let that goal guide you. 

There’s no one right answer. You can’t simply Google the skills your workforce is going to need in the next 10 years. The technology you select needs to give you access to good skill data — because the feedback this data will offer you is always changing. Stay on top of emerging skills with engaging workforce technology.

Look for a solution that keeps you nimble. “Don’t get married to some rigid skills framework that takes you a year to build,” Nolen said. “You have to be agile and iterate.” Select a solution that is as agile and flexible as your workforce.

Don’t look for functionalities, look for a partner. “You’re not just looking for a vendor with all the functionalities,” Nolen said. “You’re looking for a solid partner — one that’s going to deliver on its promises and work with you every step of the way to make sure you succeed with your transformation.” It’s always about the people. It’s not about the tech. The best partners will challenge your thinking and show you new ways to consider old challenges.

Be wary of the “yes man.” The reality is, there is no one-size-fits-all solution. Every organization is different, and they all have different needs. “If a provider says ‘yes’ to everything on your checklist, that’s bad news. Buyer beware,” Nolen said. “Even worse, if they say ‘No, but that’s on our roadmap,’ those are red flags. Avoid those who say yes to everything.” You want a partner who is honest about their solution and capabilities.

Want more guidance on how to choose the right solution for your workforce? Check out our Buyer’s Guide to Talent Development Technology. And in the meantime, watch the rest of our talented speakers from Degreed LENS Lite. 

About Amanda Nolen:

Amanda Nolen is a co-founder of NilesNolen, a boutique consultancy that drives business outcomes for global organizations through better learning & edtech strategy. To watch the full presentation, access it through the Degreed On Demand Page.

The post 6 Steps to Align L&D with Your Business appeared first on Degreed.

How does xAPI work?

We like to use a USB analogy to help describe how xAPI works. Your computer is likely equipped with a USB port, which means you can connect certified USB peripherals to your computer to transfer files, connect devices (e.g., printer, keyboard), or even back up data. As long as your computer’s manufacturer and the USB drive manufacturer formatted their equipment according to the USB specification, the equipment will work together.

The xAPI specification works in much the same way. If tools conform to the “rules” of the xAPI specification, they can, in theory, connect to different products (e.g., LMS, social learning platforms, learning experience platforms, etc.) and automatically transfer learning records. In all cases, there’s a Learning Record Store at the center, receiving, storing, and returning the data as required.

Aren’t learning and business systems already able to share data?

Not really. Previously, most learning technologies kept data locked down internally, allowing the information to be extracted only via CSV, custom connectors, or the SCORM specification.

CSVs require manual reporting work, custom connectors often take lots of time and money to build, and SCORM—though useful—is limited to very basic activity data from an LMS. xAPI eliminates these constraints.

Why do systems need to connect using xAPI?

First, without a standard format, systems are siloed, or trapped on their own islands of data. With xAPI, the info is communicated between systems with statements in an actor + verb + object format (i.e., “I did this” or “Lizelle wrote a blog.”)

Think about how many different types of sentences you can build with just those three parts of speech:

• Actor (who)
• Verb (did)
• Object (what)

You’re capable of communicating quite a bit more than just scores, completions, and duration, right?

This opens up many opportunities for the types and complexities of experiences you can capture and report on. Especially if you consider that learning happens everywhere—across many devices, locations, both online and in the real world.

If you enjoyed this article, check out the second installment in which we will explore how to get started with xAPI and what to do with your learning data.

About the Author:
A huge thanks to Lizelle and our friends at Watershed for part 1 of this 2-part guest series.

The post What is xAPI? appeared first on Degreed.

In a world where Venmo processes $51,892 in transactions every sixty seconds and that same minute means almost 350,000 scrolls on Instagram, there’s no shortage of data. What is apparent is a shortage of the ability to turn that data into something meaningful, like an insight you can act on or a decision you can make.

This is as true for learning and development data as it is anywhere. Data is a tool, and like any tool, it’s only as good as its user. So our team has put together three steps to get hands-on with your learning data and start using it to show business impact and drive decision-making.

Step 1: Cozy up to Your Learning Data

Get to know your learning data by spending some time in it. This will do two things: get you comfortable with the tools you have, and create an understanding of the data you have available to you.

Here are some examples of ways to start getting familiar with your data:

• Get all of your data in one place, like a business intelligence (BI) tool or even an Excel spreadsheet.
• Explore the analytics and dashboard pages within your learning tools, and then try to recreate some of those graphs in your BI tool or Excel.
• Try to answer some basic questions by building graphs or pivot tables, e.g. during what month over the last year did people learn the most often? What content items are the most popular with your learners?

Step 2: Work Backward From an Action

A life coach named Rob Liano once said, “If you don’t know what you want, you’ll never find it.” We’re not here to talk about existential life lessons but we do know this: that statement is definitely true for learning data. 

Data exploration — a process in which you jump into analysis with no specific questions in mind — is a great tool. But that’s generally better left for someone whose job is analysis and will have the necessary time and tools at their disposal.

Start any data analysis by defining an insight you want to draw or a hypothesis for what you’ll find, and then set out on the most direct path to find those insights or prove/disprove that hypothesis.

Step 3: Think Like a Data Designer

When you’re ready to take action on data, you might want or need to share the reasoning behind your decision with others. Data visualization designers communicate data for a living and they have great rules of thumb for this. Here are a few to keep in mind:

• Avoid tables and instead, use charts whenever possible.
• Give your charts titles that highlight the takeaways viewers should get from them for quick, succinct communication and clear value.
• Stay away from “no-no” graphs that are generally hard to understand, such as pie charts, donut charts, word clouds, and bubble graphs.

If you’re interested in learning more, Storytelling with Data, a fantastic book on the topic, is a great place to start.

Data is a commodity, and knowing how to work with data is a skill. By increasing your understanding of the data you have, using pointed questions to drive your analysis, and applying good data visualization techniques when you’re sharing your learnings, you’ll be able to take action on your learning data and see directly impactful results.

Watch the full presentation of Interpreting Data: Turn Numbers Into Insights below.

The post From Numbers to Insights: Three Steps to Make Learning Data Actionable appeared first on Degreed.

You can always find the details in the data. It’s a big reason why data science is such a necessity these days. So what is it? And why is it so important nowadays?

What is Data Science?

Today we can access infinitely more information than ever before. People are constantly interacting through the internet and smart devices are tracking every bit of our world. We call these movements and activities “data.”

To give you an idea of just how much data is out there, 2.5 quintillion bytes of data are created every single day. The most interesting (or scary) part? Over 90 percent of the data in the world was generated in just the last 2 years.

But more data does not necessarily mean more knowledge. Enter data science, the key to understanding all this new information in meaningful ways.

Data Science Applied

Data science combines three areas of expertise: business knowledge, statistical analysis, and computer science. A skillful data scientist uses their business knowledge to understand a problem, applies statistical techniques to collect data and model solutions, and writes programs to run their analysis and generate results.

It’s also how Google, Facebook, and Amazon became successful. They studied searches, similarities, friendships, and purchases to find patterns that led to profits.

Sports teams have used data science to devise Moneyball tactics to hit more home runs and three-pointers. It has even made us safer and healthier because data scientists have crunched numbers that detect fraud and disease.

Data science is a hot topic in learning, too, and for good reason. Employees use the web and mobile devices along with HR and business applications to get smarter, and in the process, they are generating valuable data on things like their preferences, their habits, and new or improving skills. Do not waste this data! Using a learning platform that makes intelligent use of data science, your organization can understand who is learning what, which resources are most helpful, and how employees are progressing.

Asking your Saas Vendors about Data Science

So what should you do about all this? Well, for starters, start asking more questions. Here are a few to get you started:

How is data collected, stored, and secured with this solution?

Data science is useless without databases, but those databases must have integrity. Ask how the information is gathered; make sure users have given consent if required. Once the data is collected, it must be maintained and kept fresh. Find out how databases are kept reliable and up-to-date. Of course, don’t forget to keep the data safe by talking through specific security threats and privacy protections.

What predictive models have data scientists built using this tool?

One core function of data science is prediction. With enough relevant information about the past and present, data scientists can forecast the future. Methods like regression analysis and neural nets use statistics to study the relationships between variables. For example, data science can predict how costs will change, when products will wear out, or what trends will become popular. When considering data science tools, make sure to find out what predictions it can make reliably.

How does data visualization help users and admins analyze their learning?

Data can be hard to digest. Tables filled with numbers? Equations stuffed with variables? Most of us don’t understand these things. But many data scientists are using data visualization to get ideas across. Creativity and beauty can completely change our understanding, helping us take in more information and clearly see how it all relates. When shopping for or evaluating a learning solution, make sure it offers elegant, intuitive displays of the data, so you can spot the trends instantly.

Now that you’re getting warmed up, check out the next installment in our data series: machine learning.

Learn More about Data Science

Ready to dig deeper into data science? Check out the resources below. Before you know it, you’ll be speaking data fluently.

Data Science, Explained for Beginners (five videos, 3-7 min each)

A senior data scientist at Microsoft gives an easy introduction, addressing what questions are answerable and how to make sure your data is ready.

Understanding the Data Science Lifecycle (infographic and 10 min article)

Here is a fresh, simple overview of the data science lifecycle, with an explanation of how to collect, maintain, and analyze data.

What Really is Data Science? Told by a Data Scientist (11 min video)

This YouTuber uses personal experience, solid research, and a sense of humor to break down the rise of data science and how it is used today.

A Very Short History of Data Science (15 min article)

This article from Forbes starts with the origins, way back in the 1960s, but the best bits come from the 2010s, tracking how interest in data science exploded recently.

Want to learn more about how Degreed does data? Read more about artificial intelligence, algorithms, and APIs.

Note: This article was originally published on 2/14/19 and has since been updated.

The post Degreed Does Data: Data Science appeared first on Degreed.

The most advanced L&D teams are embracing the trend towards informal, collaborative and social. According to the latest Bersin Corporate Learning Factbook, the best L&D organizations deliver significantly more on-demand resources like articles, videos and books, and up to 20% fewer hours via formal training (ILT, vILT, e-learning).

The general lack of insight into informal learning activities has many L&D leaders asking “How do I know employees are spending time on the right things?”

“We have to start trusting the learner. They know what they need and when they need it, and they’re going to find it,” suggested Jason Hathaway, Director, Content & Learning Solutions at CrossKnowledge.

But truly measuring the value of informal learning can be tricky. At Degreed, we believe in the bigger picture and recommend optimizing for utility and outcomes by asking ”Is the learning people are doing helping them become better at their jobs?”

How can you get an accurate measurement of how informal learning is working when results are not instant and much of the learning people do is happening outside of your company’s LMS?

Let’s say, for example, a salesperson spends lots of time watching product videos and reading about selling techniques. Certain tools allow you to capture data on the use of learning resources, but what you, as the manager or learning leader don’t know is if they are applying those ideas in practice.

So you look to their behavior and results. Are they setting more appointments? Are they closing deals faster? Are they closing bigger deals? Are their customers more satisfied? This is data you might be able to find in CRMs, ERP systems – maybe even in the talent management systems. But the one place you will definitely be able to see results (or not)? Observation.

True learning program success means observable behavior change. It’s a different way to think about ROI, but it’s a KPI’s that really matters.

Additionally, you can focus on the experiences you’re facilitating. “You can’t control what people do, but you can control the environment you provide them. Give learners easy access the best resources, including other peers, ” suggested Todd Tauber, VP of Product Marketing at Degreed.

Most workplace learning infrastructure doesn’t really work for today’s workers, partly because the current systems are built primarily for structured, formal training. But the key to empowering your learners and increasing engagement is recognizing, facilitating and measuring what’s happening in-between those formal learning settings – all of the informal learning that is happening whether it be reading an article, a conversation with a mentor or peer, attending an event, or taking a course.

Ready to start measuring your informal learning experiences? Create your Degreed profile today!

The post How to Value Informal Learning appeared first on Degreed.

This feed, and the algorithm that populates it has a huge effect on the Facebook experience. If you’ve ever unknowingly been sucked into Facebook, you can appreciate its power.

Degreed believes in the power of personalization. Engaging, personalized enterprise applications that employees use because they want to- and not because they have to, are the future.

Degreed has a personalized feed for learning content designed to target the development of each individual user. With the explosion of content, it’s getting harder than ever to weed through the noise to find the specific content you need, when you need it. The most efficient way to target someone’s development is to use technology to automate the delivery of content to each individual.

Based on user experience research and interviews, Degreed, like Facebook, continually improves its feed and algorithm. We are constantly looking at engagement and usage statistics and researching what hooks users to keep them coming back.

We’ve been refining and simplifying the user experience to make it easier for users to find relevant content they want and need to target their personal development. A year ago, action points were spread around the system. Now they are more centralized, simplifying the user experience.

What you’ll see today when you log into Degreed is one place to find all the learning you’re interested in. Based on our user research, we’ve found that more items in the feed lead to greater engagement with the content, so now you’ll see a longer list of items. If you don’t like the suggestions at the top of your list, more learning content is just a scroll away. Dismiss any item that isn’t relevant.

The Degreed feed includes system-generated recommendations from any source, in a variety of formats including articles, videos, books, and courses. You’ll see content that has been recommended by peers and managers, popular items from your network, roles you are following, content from pathways you’re enrolled in, and items you’ve saved for later.

The learning feed gets smarter the more you interact with it. Users can continue to personalize and influence the recommendations by using features in Degreed such as:

• Adding your learning interests and career goals to your Degreed profile.
• Enrolling in learning Pathways – focused on topics or skills you want to develop.
• Joining groups of people with similar learning interests.
• Saving learning items for later.
• Following people.

Organizations can influence these recommendations as well by:

• Adding content to your content management system.
• Selecting preferred providers for your organization.
• Customizing pathways for your organization and auto-enrolling employees in pathways.
• Adding roles and skills specific to your organization.

Takeaways

Most L&D leaders want to use data to improve and personalize learning in their organization. Degreed provides the tools to make this possible.

Content is everywhere, but finding and delivering the right content at the moment of need for each individual is impossible to do on your own. Let Degreed do the work of finding and delivering all the relevant content so you can target the development for each employee.

To learn more about Degreed visit get.degreed.com.

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