The Experience API (xAPI): A GPS for Learning

Learning has changed. Today, our interconnected lives are filledwith technologies and new ways of connecting to information and one another. Thisshift, created by mobile and social along with nearly ubiquitous connectivity,has evolved the world as we know it—and, more importantly, it has given birthto new capabilities for the learning ecosystem.

Today’s learning ecosystem is rapidly growing, extremely complex,and overflowing with potential data. Individual employees can access multiplesystems on and off the job for formal and informal learning—anything from LMSsto MOOCs to virtual worlds to simulations to classrooms to mobile devices toYouTube. Learning can happen nearly anywhere at any time.

Yet, while this is possible, learning the right thing at the righttime is the real challenge. Simply having access to so many systems and experiencescreates noise without creating value. Knowing exactly what someone should donext is the core of what we should be most concerned with. The key is stackingexperiences in the best order to get us to our goal.

Affordances of mobile, social, and emerging specifications like theExperience API (and others) are positioning a revolutionary learning ecosystem thatoffers individuals help in navigating all those possibilities. We are now onthe cusp of realizing the value and potential of this development.

GPS applications offer clues to anavigational model for learning

When the creators of the Global Positioning System (GPS) navigationsystem were designing it, did they anticipate that businesses would emerge fromthe ecosystem that GPS made possible?

For example, Uber relies on the affordances of the navigationalecosystem—a place where people travel as nodes tracked by sensors within maps supportedby guidance systems. But Uber is doing much more. It is building atop thesensors and flows of data at new levels in order to understand the patterns ina different way. This understanding allows the company to connect all passengerswith drivers to get them to their destinations. And if there aren’t enoughdrivers, Uber finds ways to push the pool to meet the needs of the riders—Uber’sunderstanding of patterns creates a balance within the ecosystem. This balanceis causing new value to emerge from the network. In fact, there are alreadymany business models that take advantage of the affordances of the GPSecosystem, and the number of examples is growing. 

Navigation using GPS relies primarily upon the ability to define adestination. What makes GPS navigation really smart is that it can also include:

• Maps of the terrain
• Data from other travelers beforeyou
• Data from other travelers rightnow

These data, in concert, allow some amazing things to work automagically.

What does this have to do with thexAPI and the learning ecosystem?

Are there value creators that can emerge from learning ecosystems,similar to the way Uber emerged from the GPS ecosystem?

What’s missing is one step further toward a path. A path is aseries of steps to achieve a goal. You could even say that what is needed is aGPS for learning. The real and defined need in education, learning, andtraining, across the education and corporate landscape, is a chain of steps toget you to a destination.

Steps are just sequences of locations and directions. Directionsfocus on destinations. In the classic case of higher education, for example, destinationsare typically degrees. In the case of occupations, destinations may includecompetencies. In business, destinations may be accomplishments that are theresult of applied competencies and experiences.

We also need a map, and we need information (data) from those whohave sought the same destinations. But there’s a problem.

We don’t have the map (yet)

GPS works because sensors are working in concert to produce anunderstanding of position. Because of maps, we are able to define the stepsbetween where we are and where we need to be—and our navigation system can helpus get there. We still have to work for it by driving, and most importantly, weneed to stay on the road, follow the rules, and not run into anything along theway.

What are our maps for learning? We don’t really yet have a “globalmap.” We have a lot of data, but we don’t have a global standard. Differentproducts or projects try to tackle this at different levels. Efforts like LinkedIn Skills lists, the Learning Registry and even Wikipedia are trying to map theworld’s knowledge and learning. We don’t yet have the whole map—we essentiallyhave only local versions available within a company or domain. Competencies andexperiences (content) are the key to making the map. Think of competencies as intersectionsof pathways or roads. They are the little destinations that someone can get tofrom a number of directions while the content provides the steps between theintersections.

Competency models and maps take many forms and have no unifieddata models in practice. Currently, we have the equivalent of hand-drawn mapsof villages or towns. We have “cave-like” line drawings of steps as well, suchas in onboarding. We need better ways to describe and map competencies (yes,and knowledge, skills, behaviors, and measures) and their relationship tocontent or experiences.

Projects like the Learning Resource Metadata Initiative (LRMI) and numerous previous attempts at mapping digital objects andmetadata (Handle System, Dublin Core, IMS Learning Resource Metadata, and others) aim to attack the mapping problem fromdifferent angles. The Simulation Interoperability Standards Organization (SISO) is alsostandardizing further work on modeling human performance using HumanPerformance Markup Language (HPML).

Although work has been done, and continues to be done, a globalmodel isn’t enabled without a real way to describe the intersections and roadsin one common language. The AdvancedDistributed Learning (ADL) Initiative believes there might be a way to standardize andassociate the maps. ADL’s new project, the Competency and Skills System (CASS), aims to provide a largerframework for associating competencies.

These are great moves forward, but we still need interoperabilityof this data to connect intersections and steps to make a real global map. Withoutthe real map, we will just have relative positioning. Allow me to offer anotherset of examples that may give you a better picture of what is involved.

Destination ISS (the International Space Station)

Depending upon whose definition you use, about 550 people havevisited space; 222 of them have visited the International Space Station. As oftoday (April 25, 2016), there are about six people in space. Dreaming about getting to space and actually getting there are two differentthings. There are many common destinations in the paths of each of the peoplewho have been to space, but there are also many different paths. Wide varietiesof military and academic backgrounds in different countries all led to a set ofcore experiences to allow this group to do what few have done.

In the same sense, there are pathways to travel through educationand experience, and many intersections and many turns exist. Though peoplestart in different places and take different roads, they eventually end up atthe same intersections and same steps.

Setting a destination is important to any journey. If learning isgoing to be like a journey, we need to be very specific at definingdestinations. If we have a map and destinations, getting there will be quite abit easier. The path to get there will be clearer, and knowing where we can gofrom any point will be much more obvious. But first, we have to know where weare.

Position: Where am I?

Of greatest importance to GPS is the latitude and longitude grid. Thisgrid tells us where we are (and also where we are not). The corollary inlearning is the profile. The profile includes a history.

With the xAPI, we have a way to describe the different waypoints(landmarks) in a person’s history, places where they have been (experiences),and intersections or destinations (competencies) they have passed through. xAPIdata can capture the step you are taking (content) and also information such asyour performance (context) toward an intersection (competency) to get to adestination (course, badge, degree, job), even if that’s a job on the spacestation.

Schools provide destinations

What are schools? They are groups of people who have a “recipe”that they have agreed upon and that has created outcomes or destinations thateveryone agrees are generally good. And the people who have experienced theoutcomes then become evangelists for the outcomes or the pathways.

A school is a set of learning experiences that have a highprobability of achieving a desired result. That is, these experiencesconstitute a collection of curated and potentially productive pathways toward adestination.

Think of a school as a map to get from where you are to a common destinationwhere others have already gone. Most importantly, it is a place from which onecan move to the next destination with a high degree of probability.

Now, there is a deeper opportunity.

There are many destinations in the world, such as being a doctor,being an artist, being a biomedical engineer in Austin, Texas, or being like myfriend Pat. More importantly, questions such as “How can I be like a certain co-workerwho has X, Y, and Z skills that I don’t have?” are quite interesting. Mostimportant is knowing what you don’t know that can get you to your destination. It’sreally all about skills acquisition. Skills come from knowledge and practice tobuild ability. These all come from the right sequence of steps or experiencestoward the destination.

What are the destinations that we define in life? Where do westore them? The answer to both questions is “many places,” and that brings usto the important problem of data interoperability.

Data interoperability across theecosystem

New efforts are emerging around datainteroperability in this future ecosystem. Aaron Silvers and Megan Bowe of MakingBetter established the Data Interoperability StandardsConsortium (DISC)in October 2015. They are working towardsomething bigger, building out from the xAPI in a way that will cut acrossindustries and beyond L&D.

“Since 2014, there’s been an industry effort to move xAPI forwardas an industry. … The spec needs an organization that addresses the biggerworld xAPI is part of,” says Aaron. He is referring to the fact that maps orpositions are just part of a much larger opportunity to create the GPS forlearning. xAPI is one component, but we need more.

Regarding building the future of GPS-likeecosystems, Aaron says, “With data interoperability we’re looking to enable adata environment where we can successfully design, build, and grow systems oncommon expectations of data. This enables increased interoperability, dataownership, and advancements in analytics.” DISC is focusing on three maingroups: tool providers, professionals who build and design systems, andindividual data owners.

Aaron adds, “The focus for data interoperabilityis larger than xAPI, but obviously xAPI is a known entity with plenty going forit and a lot for all of us to work on, so our goals for now are to make workingwith xAPI easier for stakeholders, developers, designers, data analysts …everyone.” DISC will be focusing on software certification in 2016, moving onto a certification program for professionals who develop learning record stores,and then focusing at another level on additional professional certificationprograms.

GPS and experiences

Explicating the destination is important. Acquiring knowledge,skills, and abilities on the way toward the destination comes through formal, informal,and experiential learning.

Thirty years ago, we had “great” paper maps. Fifteen years ago,traveling was a bit easier as you’d print directions from MapQuest or othersources. This allowed you to get in the car, drive, and use the specific stepsand guidance directions and more granular maps. If you got off course you’dhave problems, and depending upon the town, potentially real problems! Today,the world is quite different because of mobile and persistent connectivity thatprovides constant adjustment of position and course. With many other nodes inthe system, we have far better maps and can predict success or failure on anygiven route using data from many nodes simultaneously.

Why isn’t this functional evolution more apparent in our learningsystems?

Learning systems can and should be a lot more like GPS. They needto know where I am. They should allow me to set a destination. I should have anidea of the path or route. Based upon my preferences or other choices, I shouldbe able to affect the route. When I’m ready to move ahead, based upon the mapand other data from actual “travelers,” it should get me there via the best waypossible.

Ecosystems and GPS

With the explosion in technologies and instant access toinformation, the opportunities for learning and the availability of learningdata are massive. Systems acting in concert could be like an ecosystem tosupport learning. In much the same way that GPS supports our wayfinding, asystem could support our guidance for learning. With all of this, we could understandthe best way people have gotten there and the best way people are getting thereright now, including the challenges and obstacles of the day.

Learning ecosystems are essential to capitalize on this web ofdata to enhance learning experiences. By placing “sensors” within learningecosystems, we are able to create a GPS-like ecosystem. The data provided bythe new technological age has the potential to propel learning and enablelearning ecosystems to deliver the right learning experiences to the rightpeople at the right time.

Weare about to change the game through interoperability of data about humanexperience as the xAPI expands in the world. We need to keep finding ways towork together to bring the GPS for learning into the world.

From the Editor: Want more?

Watch the video of Mike’s talk on learning ecosystems, given March 17, 2016 as part of the xAPI Camp held during The eLearning Guild’s Learning Solutions + Eco Conference in Orlando.