- Get Started
- Write Code
As a technical specification, the relevance of Tin Can to developers is obvious, but Tin Can is equally important to learning designers. You don’t need to understand all the details of the code, but you do need to understand the possibilities and how it all fits together. Tin Can takes instructional design out of the SCORM box and enables a whole range of possibilities for the learning solutions you create. It’s important, and you need to know about it.
The Understand section of this site explains Tin Can and why it’s important. If you’ve not read that section yet, you should go back and do so. You’ll read that Tin Can enables us to track any learning experience, supporting not just formal training inside the LMS, but real world training, informal learning, social learning and even job performance.
Tin Can enables the creation of products that enable you to do the things conference speakers have been saying for years that we should do, but we couldn’t because we didn’t have the tools to make it practical. Some learning technologists are predicting, for example, that as Virtual Reality becomes a consumer grade technology with a price tag to match, we’ll see a huge growth of virtual reality within learning technology, similar to the growth previously seen with mobile learning. Tracking learning experiences utilising this and other new technologies will rely on Tin Can.
You’ll also read about the power of Tin Can to support learning analytics. It’s now possible to know which of our learning solutions changed behaviour and led to positive outcomes in our organizations. You can get data that proves the investment that you’re asking for is worthwhile. You can also see the activities you’re doing that aren’t worthwhile and stop doing them! This is a really big deal.
This new world requires a new way of thinking, a resetting of assumptions, a paradigm shift. Many instructional designers have only ever worked with SCORM style e-learning that’s zipped up and uploaded to an LMS. With Tin Can, this kind of e-learning might or might not form part of your solution. Either way, Tin Can enables you to think broader than courses in your role as an agent for performance improvement in your organization.
Let’s take a look at some important learning models and theories and how they relate to the new world of Tin Can.
To really get into a Tin Can mindset, you need to think of your learning solutions as a collection of layered and linked experiences. At a high level, these events are things like working through an e-learning course, attending a conference, or completing a business process. Each of these contain smaller experiences: interacting with a slide, attending a seminar or completing a task within the process. For each experience within your solution, think about what useful data could be collected about that experience, how the experience might be altered based on data from other experiences and how data from different experiences might be compared within your analytics.
This is a big difference from the SCORM mindset. Where SCORM is all about statuses, Tin Can is about events. SCORM, for example, tells us at a given point in time, the success status of a learner for a course. Tin Can, on the other hand, records that the learner passed the course at a particular point in time. SCORM will tell us which learners have passed which courses; Tin Can tells us not just that they passed, but their journey of learning through failures as well as successes to get to that pass.
Learning analytics design is an emerging field within learning technologies. Where SCORM reports tend to be relatively simple, the range of data sources available to a Tin-Can-powered analytics tool means that quite complex analytics reports are now available to answer important business questions. Analytics design is needed to find and answer these questions.
The first step in analytics design is exploring the questions that are important to the business. There’s no point designing analytics to answer a question that nobody wants to know the answer to. Take time to work with stakeholders to define the important questions for learning and development in your organization. Make sure they are the kind of questions that will lead to specific actions and directions. Examples of good questions include:
With your area of questioning determined, decide what you think the answer to the question is, and plan out what data you can capture that will either prove or falsify that position. How will you know if your hypothesis is correct? How will you know if you were wrong? Make sure that you will be able to get access to the data sources required to answer your question.
Next, you’ll need to design and implement your experiment. Will you assign learners to groups in order to test different sides of the hypothesis, or will you allow learners to self select their preferred approach? How can you test a pilot approach as realistically as possible without investing too many resources into the solution before you’ve proved whether or not it works?
With a plan to collect the data in place, think about how you will analyse that data to answer your question and test your hypothesis. What graphs and charts will you need? Will different stakeholders need different data presented in different ways? How will you dig deeper into your results beyond your initial question?
This page has dug into the implications of Tin Can for learning design. Read on to dive deeper into these implications and some practical applications for our learning design processes.
We help organizations to implement their learning analytics projects to provide actionable insights from their training and performance data.