Data, machine learning, and artificial intelligence are disrupting everything you know, in ways that most of us don’t understand — yet. To help you keep up, we’re launching a new series, Degreed Does Data. We’ll dive deep into these topics, simplifying their complexities, explaining their potential impact, and breaking down the dramatic ways they’ll be changing the industry.

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?

Today we can access infinitely more information than ever before. People are constantly interacting through the internet and countless 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. Data science is the key to understanding all this new information in meaningful ways.

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.

Data science is 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.

So what should you do about all this? Well, for starters, start asking more questions. Here’s 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 specifics 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 know getting warmed up, stay tuned for the next installment in our data series: machine learning.

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.

Part one of our 2-part xAPI series taught us the basics of Experience API (xAPI) including the definition and how it works between systems. This post will explore getting started with xAPI and the data that comes with it!

Do I need to build it myself? How do I get started?

Good news! If you’re an L&D professional (not a vendor), you have many options in getting xAPI enabled in your organization without doing much, if any, coding work. First, you’ll have to make sure you have a Learning Record Store where your data can be stored. There are many free and paid versions with varying capabilities.

You’ll also need to choose the tools you want to use or evaluate your existing learning tools’ capabilities. There are currently hundreds of tools, such as an LMS, social learning platforms, authoring tools, and more that are already equipped with xAPI. You simply need to ask your vendor if and how they support xAPI. In the case of existing systems that don’t support xAPI yet, you can still bring that data into an LRS using a data converter or third-party connectors.

What am I supposed to do with all this new data?

More and more organizations are using xAPI to connect learning technology products to build the learning ecosystems they need. When applications already support xAPI, integrations can be as simple as plug and play.

Once your learning systems are integrated and all your learning records are stored in one place, your data is perfectly primed for learning analytics. You can start exploring your data to get a clear picture of what’s happening in your organization. Evaluate learning programs and explore the reasons behind the most popular or successful training. And, once you’re armed with a better understanding of these programs, you can begin to positively shape and enhance your learners’ future experiences.

To learn more about xAPI, access helpful resources, and explore how other L&D practitioners are using it, visit xAPIxAPRIL.com.


Lizelle_Holstein

About the Author: Lizelle Holstein, Director of Marketing at Watershed

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

For most of us, the arrival of Artificial Intelligence (AI) seems sudden, out of the blue.

Just a few years ago, your friends weren’t automatically tagged in images you posted on social media.  You couldn’t talk to Siri or Alexa.  And pedestrians weren’t getting struck by self-driving cars.  The speed with which AI has begun to transform our digital and physical worlds has been breathtaking.

Like most disruptive forces, we’re not yet sure what to make of AI.  Does it herald the end of human drudgery or the beginning of human obsolescence?  Will AI create more jobs than it destroys over the next 10-20 years?  Is Elon Musk right that the uncontrolled advance of AI represents an existential threat to humanity?

Before we assign sinister or benevolent traits to AI, we should take a moment to dig a little deeper and perhaps uncover AI’s true value.

AI has the equivalent of a large ‘family tree’, with many branches.  There’s been a great deal of investment in some technically very sophisticated branches, such as machine learning.  But when it comes to human learning, I’d argue that there’s a less-appreciated branch of AI with tremendous, immediate potential to improve the way people learn.  It’s called Natural Language Generation, or NLG for short.  Simply put, NLG is computers writing stuff.  It’s the branch of AI that automatically turns data into written language that humans can read, understand, and even enjoy.

Since the printing press was invented, we’ve found ever-better, ever-cheaper ways to get the written word to large numbers of people.  But there have always been two major limitations.  First, humans have always written everything, and writing takes a lot of time.  Second, the words published are uniformly the same, even though the individuals reading those words are uniquely different.  For centuries, it’s been this:  A person spends a lot of time crafting a single set of words, then a great variety of individuals – who may differ wildly from one another – reads those words, and the writer hopes it resonates with each individual.

But thanks to NLG, we can now dramatically speed up the writing process – as in, writing hundreds of pages per second – and we can now vary the words written in each case.  NLG allows us to tailor written language depending upon certain variables, so that not every person reads the exact same thing.  This means the more we know about someone, the more we can tailor written content so that it’s hyper-personalized and relevant to that particular individual.

My company, CredSpark, is an interactive assessment platform enabling learning companies, marketers and media firms to ask questions of their learners and readers, in order to generate insights and catalyze action.  CredSpark is a proud partner of Degreed, and we’re also among the first companies use to NLG in a new way:  To generate personalized recommendations to individuals based upon what they’ve told us about their knowledge and interests.

Our initial work with NLG personalization has been around professional conferences: how to make a large trade show ‘feel small’ by asking an attendee a few questions and then generating a written recommendation of the sessions, exhibitors, and products most relevant to that person.  The response among attendees has been extremely positive.

But we’re equally excited by the possibilities around personalized recommendations for professional learning.  Imagine you’ve arrived at a website with a long list of learning resources: articles, videos, webinars, etc.  Instead of having to spend lots of time filtering and scrolling, what if you could simply answer a few questions and have a ‘short list’ of the most relevant resources tailored exactly to your knowledge and needs?  Even better, what if it wasn’t a list, but a narrative, providing you with context around why these particular resources are relevant to you, thereby giving those recommendations real meaning?  That’s what NLG + assessment can deliver.

We think this is the true power of AI in learning:  The ability to deliver individually-tailored learning guides containing only the most relevant resources, wrapped in a narrative that conveys meaning and value to the learner.  People learn best when highly engaged, and there’s no better way to engage learners than with learning plans which reflect their unique identity and needs.  Further, such personalization can support learning among people with varying levels of prior familiarity, and who learn at different paces.

It’s our hope that Natural Language Generation, combined with interactive assessment, will be widely adopted to scale the delivery of personalized learning journeys, thereby making each learner the hero in the narrative of her own advancement.

As many of you know, data handling standards continue to evolve around the world. With that comes big responsibility. Degreed is committed to being worthy of your confidence in that your information is safe with us.

In the business of learning, we’d like to shed some light on the state of data protection.

As of May 25th, 2018, all organizations that are a part of, or process the personal data of EU citizens, are required to comply with the updated General Data Protection Regulation (GDPR).

What is GDPR?
The General Data Protection Regulation (GDPR) is a regulation that is intended to strengthen and unify data protection for all individuals in the EU. This regulation gives more control to EU citizens over their personal data and becomes enforceable on May 25, 2018. The requirements are too lengthy to go into great detail, but in short, it allows users to explicitly opt-out of having their information gathered, sets stipulations regarding timely notification of data breaches, ensures right of access and erasure, data portability and a few other items. We are working with our Dutch counsel to understand the GDPR requirements and ensure Degreed remains on target to meet the compliance date.

What does this mean for our clients, prospects and colleagues?

Degreed is pleased to announce that it has obtained EU-US and Swiss-US Privacy Shield certifications effective March 6, 2018. This certification shows that Degreed adheres to the principles of both Privacy Shield frameworks, commitment to data protection and privacy for all users. Degreed also remains committed to reaching GDPR compliance in advance of the May 25, 2018 enforcement date.

We are committed to supporting the enterprise with GDPR requirements including:

  • notification of any security incident/data breach involving their users’ data,
  • ensuring safe transfer of data,
  • supporting enterprise with user requests to remove data, and
  • supporting enterprise user requests for portability/export data in cases

Degreed’s responsibility is to support the enterprise’s need to meet the requests of their users. Additionally, Degreed has entered into Data Processing Agreements which outline roles and responsibilities as well as shared obligations between Degreed and the Enterprise. It’s important to note that client organizations are still obligated to adhere to GDPR guidelines as the Data Controller, and Degreed has less direct obligations as the Data Processor.

Please reach out to your organization’s Information Security team for specific details to your organization and you can find more information here: https://gdpr-info.eu.

 

 

The Experience API (xAPI) is a technical specification that makes it easier for learning technologies to connect to each other. Basically, it’s a rulebook for how learning tools communicate about online and offline activities of an individual or group of people.

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 or two, 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 had 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.

Stay tuned for part two where we will explore how to get started with xAPI and what to do with your learning data!


lizelleAbout the Author:
Lizelle Holstein is the Marketing Director at Watershed.

 

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

According to the creators of Scrum and its body of knowledge, the Scrum Guide, Scrum is a simple framework for effective team collaboration on complex products. Scrum consists of Scrum Teams (a Product Owner, Development Team, and Scrum Master) and their associated events, artifacts, and rules.

scrum

As successful organizations continue to nurture their ability to deliver with greater agility, they are increasingly turning to the Scrum framework to improve the way their teams work.  When applying Scrum, teams work together to continuously inspect and adapt how they work.

Even more good news

Scrum.org and Degreed have partnered to make learning and developing your Scrum skills even easier! The agreement will enable enterprise employees with a subscription to Degreed to learn general Scrum topics and those specific to their roles on the Scrum Team, helping organizations and individuals deliver higher value products.

By partnering with Degreed, Scrum.org has opened up an avenue for individuals on Scrum Teams to evaluate what they know (inspect) and continually learn (adapt) to enable continued professional growth.

“We are excited to have found a partner in Degreed who, like us, is focused on improving how people work in professional environments,” said Joel Lamendola, Vice President of Business Development of Scrum.org.  “By partnering with Degreed, we can bring Scrum learning paths to individuals within their enterprise clients to help those individual Scrum Team members become more effective in how they work within their Scrum Teams.”

To learn more about scrum and visit Scrum.org for further information on the organization’s Professional Scrum assessments, training, and global community; follow us on Twitter @scrumdotorg and read more from our community of experts on the Scrum.org blog.

User-Generated Content (UGC)

Short for user-generated content, UGC is the term used to describe any form of content such as video, blogs, discussion forum posts, digital images, audio files, and other forms of media created by consumers or end-users of an online system or service and is publicly available to others consumers and end-users.

“UGC – user-generated content.” Beal, Vangie. Webopedia. February 2018. IT Business Edge. https://www.webopedia.com/TERM/U/UGC.html (accessed February 2, 2018).


In a Learning & Development context, user-generated content (UGC) is unofficial educational content created in one person’s area of expertise for others to learn from. UGC can be an article, a video, an infographic, a chart, or any other representation of information.

Some UGC is internal, on your company intranet or wiki sites. Other UGC is public, on sites like YouTube or Medium that allow users to share content they’ve created. If you choose to use UGC, you can rely on internal content, or curate public UGC.

UGC can help you promote peer learning and learning with technology. Internal UGC transforms employees’ institutional knowledge to collective wisdom distributed throughout your company. You no longer need to limit your L&D offerings to topics you have instructional design time for. SMEs can recommend public UGC when it exists or create UGC, freeing your L&D team to focus on the highest-value skills your organization needs.

Next post: Resource

As Learning & Development departments adopt more technology to further their mission, new terms spread dizzyingly fast and useful terms float in a sea of jargon and buzzwords. To help you stay on top of the trends, Degreed is launching a new L&D Dictionary blog series.

In each installment, we go over the traditional or dictionary definition of an L&D term before going on to explain its significance to the modern learning world. Armed with these definitions, you can cut through the hype to apply new concepts to your training offerings so your employees remain on the cutting edge.


Curation
cuˈration, n.

 1. The action of curing; healing, cure.
2. Curatorship, guardianship.
Draft additions  1993
b. The supervision by a curator of a collection of preserved or exhibited items.
“cuˈration, n.”. OED Online. January 2018. Oxford University Press. http://www.oed.com/view/Entry/45958 (accessed January 19, 2018).


Curation is one of the hottest topics in Learning & Development, but dictionaries haven’t quite caught up.

Degreed defines curation as the process of evaluating, organizing, and sharing learning resources around a specific topic while adding context with your own instruction to create a personal, relevant experience.

If you’re new to curation, where do you start? Curation is a valuable tool you can use to provide more tailored instruction to employees with the same limited time you have.

Traditionally, if you wanted to share new material, you had to analyze the need, find Subject Matter Experts (SMEs), interview them, design your learning activity, draft material, review it (with your SME, whose time is also limited), provide the materials, collect feedback, and (hopefully) update the material for next time. That meant each content area was a real commitment, and lots of emerging topics just couldn’t make the cut. In today’s dynamic learning landscape, you need to move faster to help employees keep up with the ever-changing nature of their work.

When you curate learning content, you don’t have to create all-new materials yourself. Instead, seek recommendations for relevant materials from SMEs or research to find some yourself. These can be from professional organizations, luminaries in the field, or your SMEs’ own materials shared online. By combining content from other sources to cover the general portion of your material, you leave yourself more time to create new content where it really counts—about organization-specific processes or concepts.

Interested in learning more about curation? Check out this Degreed Pathway.

Next definition: User-Generated Content

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