Tim Martin has been working with SCORM for years, listening to people’s experience and problems and thinking about its limitations and future. Given his experience as a key player in Project Tin Can, Tim is here today to advocate the values of Tin Can, share a few concrete project examples and show us how the future of Tin Can is going to be awesome…
First things first: What is Tin Can?
Tin Can is the answer to SCORMs problems.
SCORM is a two-party system consisting of an LMS and some content, with standards about how it all fits together and how it works. SCORM is able to report in a simple way about the formal learning activities a formal learner undertakes. For example, tell us how many people followed a particular learning module. That’s it.
What is wrong with SCORM?
SCORM is limited because it can only tell us how or when one particular learner logged into an LMS to take a prescribed piece of training in an active browser session. If you read back the last sentence, you will see that it is fully loaded with all the problems of SCORM. That is not how we learn and that is not how we as organisational L+D people want learners to learn….
With all the hype around 70:20:10 and non-formal learning that takes place in the organisation, it seems clear that the majority of what people learn doesn’t come from classical training or formal learning solutions like the e-modules or video that SCORM has been measuring. The majority of learning is not coming from one person (alone) logged into one specific LMS system (if any) to follow a prescribed event (eg training) at one specific moment in time. People getting a lot of content from a lot of different places, sharing a lot of ideas and they are definitely learning in a less formal way.
And many L+D people today don’t want to oblige people to login to one particular LMS system to control their learning in a formal way. Martin cites the example of Google who told him “We don’t want an LMS. We don’t want people to have to do specific controlled things in a specific controlled way. We just want them to go out and learn.” But Google also wants to be able to see what is learnt and how it impacts performance. Enter Tin Can API…
How does Tin Can work?
Tin a Can API is a shared language for systems to talk to each other about the things that people do. It consists of an “activity provider” (whatever system it might be) telling what people did (whatever it was) and an LRS (learning record system) that listens and records. It does this with a simple noun-verb-object approach that records all activities and puts them in the LRS.
This modern web-service based system easily allows different systems to collect information. Here is a list of use systems that have already adopted Tin Can as their standard. Theoretically, Tin Can API can capture everything that is going on. And then correlate those activities, run analysis and give insights about what is going on. Across different systems.
The “activity provider” will report on (learning) activities across a variety of systems, which will then be stored in the LRS. This information can then be compared to data about performance from other non-learning systems. The LRS will be searchable (“bigdatable”) and could be used to draw all sorts of conclusions about learning and performance.
SCORM can only tell us a little bit about learning activities, mostly about completion rates, sometimes about test results (eg Tim followed training module X). Tin Can will go much further, allowing us to capture almost anything at any level. Martin gives an example, comparing to a SCORM system that can (only) tell us that 6 learners completed a CPR module and scored average 68%: Tin Can will be able to tell us how many times one learner compressed the CPR test dummy during the simulation, where he put his hands and the impact that had on the reanimation process. It will be able to produce a massive amount of (big) data and analyse everything, looking for trends and giving full reporting on the correlations between different learning activities/results and, eventually, performance.
But it goes SO much further than this still formal learning reporting…
It may be awesome, but give me a practical example of this awesomeness please…
Imagine the following: Google employees pick up content from across a variety of systems. They search, they consume and then they share content on platforms like LinkedIn, Yammer (or whatever Googley thing Googles use). Let’s pretend they are sales people. They then go out into the sales world and makes sales (or doesn’t).
Tin Can will allow the Google L+D people to run analysis at a very detailed level on all the different (learning) content that was picked up by all the different people. Add into the mix reporting on who searched and shared what, how, where and when. Who liked something they read or retweeted it. Tin Can will then allow us to correlate all that information with sales performance activities and data (again from different systems) in order to draw conclusions about the acquisition of knowledge and skills and the impact on sales.
Example: Do people who learnt how to ask specific questions in a sales meeting close more deals? Do people who called back their prospects within 2 weeks of meeting them close more sales than those who didn’t? What key words are top sales people searching on their browsers? Is there a correlation between the number or type of sharing on social media platforms and the sales closed. If so what?
The possibilities for data collection and analysis with Tin Can are endless, given the simplicity of the way in which the “activity providers” report on what is being done (see below…). With such information, learning people (and managers) will be able to focus more on the learning the organisation needs to bring the results it is missing.
Personally, I find this very exciting (others more cynical might imagine the scary dark-side applications of such systems). I already wrote about “Big Data for Learning in a Call-Centre” but didn’t realise the standards were there. Even though Tim Martin has repeated several times today that it’s not all there already and that we need to move slowly, it is clear to me that this will go very far…
Thanks for reading