The Tin Can API is a big change. In every way, Tin Can is different than any other standard and standards effort that has come through our industry. Hopefully after reading this you will understand the basic parts of Tin Can and discover something that resonates with problems you are struggling with.
Tin Can liberates data about the things people do, learning and beyond, from the many systems they are locked in.
As a technology in and of itself, Tin Can API is plumbing; it’s not something that learners should ever have to deal with. It sits behind the scenes, simply organizing so when recording an event or some interaction with technology, the data is consistently stored as Actor : Verb : Object.
● Someone did something.
● Megan read this article.
● Bill posted a comment.
● Aaron completed a test with a score of 95.
These sentences don’t inherently have meaning outside of their context. You can’t measure learning with a ruler, a cup, a tape, SCORM, or Tin Can. Learning is in people’s heads, you can measure what they do to suggest knowledge, interests, and goals. Trajectory is a great way to view this, what path are they succeeding on and how can you help to encourage or shape that.
You may know of the Dr. Seuss book, Marvin K Mooney Will You Please Go Now!, where Marvin is told to “go,” emphatically, by a giant hand. The giant hand is very demanding that Marvin go; it doesn’t care how. Walk, bike, ski, Crunk-Car, Zumble-Zay, camel in a bureau drawer, and other funny Seussian words are all options for Marvin to move from where he is to where the giant hand wants Marvin to go (“to bed,” one could assume). The giant hand is only concerned about Marvin and, I’m assuming, has abundant time to spend with just him.
If there were 30 Marvins who all needed to move at once, this would be a problem of scale: the giant hand would have trouble keeping track of where each Marvin would be.
Not unlike Marvin, there are many of us in the learning profession who are told by other parts of the business how they want employees to learn and, sometimes, where they want them to be in the end. This is frustrating to those of us who want to help people get to where they need to be or to prove they’re already there, without beating fellow employees over the head. No one wants to make a person sit through a course covering material they already know for that one piece of information that makes it possible for them to pass a test. The businesses and organizations that employ us really just want to know that employees made it where they need them to go; how is why they employ us — to help them get there.
Back to Marvin K. Mooney… widely adopted standards like the Sharable Content Object Reference Model (SCORM) made it so that the metaphorical giant hand could reliably receive reports only on (for example, in Marvin’s case) walking. Walking had to be done in one environment. Walking has a record of when it starts and when it ends. It has a score (pace or time, perhaps). Walking also would have a status of being complete, Marvin made it to the end of the walk. That doesn’t necessarily mean Marvin made it to where he needs to be, but the walk is over, it’s complete. Now, this isn’t to say that Marvin couldn’t use the other modes but they would not be treated the same as walking in the system. Those types of activities could not speak in the same language that walking could, so they could not be recorded or reported on.
Now, swap the word ‘walking’ for ‘a course’ and you have reality.
Enter Tin Can. Suddenly, tools that support all of the other ways that Marvin can move are now at the table. They are able to communicate in a way that the other tools can comprehend. This is especially important when there are multiple Marvins to support, in context. Marvin can choose any mode he wants to get from where he is to where he needs to be because they all communicate in a consistent way about his actions. The giant hand can collect statements of each Marvin’s activity, if and when each Marvin chooses to share.
● Marvin arrived at bed via Crunk-Car
● Marvin arrived at bed via Zumble-Zay
● Marvin arrived at bed via walking
● Marvin arrived at bed via skis
To be more exact: in a Tin Can world, if a device, app, environment, tool, or network interface makes Tin Can statements, such activities can be recorded in a Learning Record Store (LRS) and the data about those activities can be used to give people better insight into which learning activities are happening, how they’re happening, and where (context is very important).
Vocabulary: a statement is a record of an activity that matches the Tin Can API spec for activity data in JSON format. A learning record store is an application that receives and shares these statements (like a database, but with more capabilities needed for Tin Can than a typical database has built-in).
This type of insight is happening at this very moment. There are people, right now, building interfaces to help you make sense of this data. While the Tin Can API specification doesn’t standardize what the data means or how you should use it, what Tin Can does is define the structure for consistent, clean and normalized data and lets systems talk to each other in that shared language. This makes it possible for amazing insights to happen against the data from many systems.
There are activity providers who make statements: these are the apps, interfaces, content and/or devices with which a person interacts. To make a statement, a developer with access to the code of a tool a person might use specifies when a statement should be created and sent to the learning record store.
The learning record store can keep these statements and additionally give you a way to gain insight from the data through reporting or visualizations. It can also share the statements with other learning record stores if requested.
Are you telling me to track everything anyone ever does??
No. The person designing the experience to be interacted with is one of the best people to say what is meaningful activity and what is not. If that’s a quiz score, it can be a Tin Can statement (if that’s really what you need). We can record as granular an activity as one can define. It’s completely up to the designer to pre-define which actions will indicate that a person is or is not on a track in a given context, even if it’s the person who’s learning and performing is on their own self-directed path. Many times, looking at a person’s statements we can tell where their interest lies and offer them learning experiences that are similar to those interests.
What designer wouldn’t want to offer someone an experience closer to what they’re already interested in?
Building on the Marvin example above, let’s say Marvin always walks to bed and it takes him 20 minutes. You decide to teach Marvin about biking to help him get to bed faster. This may be a scenario where you want to record more granular information because it could show whether or not your teaching has been successful and can now attempt to do something he hasn’t done before. This is a new behavior that may improve his performance in making it to bed.
● Marvin experienced instruction on biking
● Marvin started riding a bike (All of these could include time and location)
● Marvin stopped riding a bike
● Marvin arrived at bed by bike
Now we have an instructional element, followed by a series of activities that take place afterward.
With meaning-making capabilities that make use of all this Tin Can data we’ve collected, we could compare the new time-to-bed with the previous day’s time-to-bed and see if biking has improved the time. If the data shows that Marvin stopped riding the bike halfway to bed and finishes by walking, this could be an indicator that he needs more support to perform the activity or perhaps he doesn’t like biking. Both of these inferences would be important to surface: there’s nothing about using Tin Can that releases us from the responsibility of talking with people about their experiences to explore what they’re struggling at or unhappy with.
There’s a million things that could happen; surfacing them makes it possible to have better conversations about how to help.
I don’t want people recording all of that about me!
I want to provide a dose of reality for you: any good website with a user experience group worth their salt is already recording much of this information about your activity, on the sites you use regularly. They track a ton of interaction data to continuously improve their sites, just like learning department should with their content. Social networks use a very similar specification which inspired Tin Can, called Activity Streams. That records all of this kind of information about your activities on those sites. We just can’t get to that data on most social networks, nor can we do much with it even when we have access to the data itself.
Why are we recording activities?
Enter activity theory with Vygotsky and Engeström (and you thought you were tired of hearing about Marvin!). Activity Theory points to action as a sign that a person successfully identified and cobbled together the right pieces of knowledge to perform the activity. This can give a lense to tacit knowledge. It can also directly link learning activities to real workplace performance. I’ll save you from a deep dive here, but if you are brave read on here.
While this is not part of the spec, I want to suggest something for us all to consider. The actor (the person actually using technology to do things) should be able to collect his/her own information about his/her activity and share this information at his/her/their will. Maybe this data would prove to an employer that Sally Employee already learned about a ton of things informally, inside and outside of work, as proof that she should be considered for a promotion or assigned the new cool project on her team. If someone is inspired by something and have taken the initiative to dig in and learn about it, regardless of the environment that should be something she can show.
So How Do I “Can” My Work?
See that? I’m funny.
You can find a list of adopters here. They are spread across the realm of learning and performance support. If you want to start making use of Tin Can or you want a tool you use to adopt, start the conversation. There are many people out there who are willing to help. We all have a say in how this story will go and end. Advanced Distributed Learning (ADL) is running this spec effort with the community, and everyone is welcome to join. Together we’re better.
Ask for more from your employer, your clients, your vendors, your government. Ask more from me. I’m not perfect and surely I’ve missed something you care about. Please, ask questions. Have the conversations, push the ideas and pieces you care about. Ask for learning data to be liberated from the silos it’s stuck in now: help everyone be better.