Using data can be one of the most intimidating parts of being a learning designer. Facts, statistics, or just random information. Our challenge is to take what is hard to learn or just boring and turn into something meaningful, memorable, and engaging.
There are four strategies that you can pull from to help accomplish this task. You've gotta HAVE it:
Let's walk through what that might look like. Here's a random statistic I pulled from the internet today from Research.com:
68% of employees prefer to learn or train in the workplace.
58% of employees prefer to learn or train at their own speed.
49% of employees prefer to learn or train when necessary.
Humanizing data means giving the data a human form, building emotional connection. Storytelling is a great way to humanize data.
In this case, it would be very easy to show a team of three employees talking to their manager about their training needs. 68% could be rounded to about 2 out of 3. 58% could be about 1 out of 2. 49 % is about 1 out of 2.
Here's a brief example of how this story might play out:
Manager: Hi, team. I wanted to talk to you about learning and development today. How are you feeling about your skills? How do you feel we can best support you in career growth as an organization.
Employee 1: I'd love the opportunity to learn more about instructional design.
Employee 2: Yeah me too!
Employee 3: Honestly, I'd just like more time to focus on work. I can learn on my own time.
Employee 1: I don't agree, but what I'd love is more training I can access when I need it or on the job training that happens when I'm about to do a new task.
Employee 2: I don't know I like learning before I need it so I can be prepared when it comes along.
Employee 3: I have no opinion on how we learn.
Employee 3: Can we agree that learn-at-your-own pace style content is better?
Employee 2: Mm... no, I want to be guided through things. I love the in-person workshops and vILTs we do here.
Manager: Ok. That's a lot for me to consider. Let me think on it and get back to you all with a plan.
Here, we come away not seeing just the data-- the numbers and the statistics-- but we see how that data is lived out in the work environment. L&D is challenged because different people have different needs and wants, and they have to make their training worthwhile and accessible for all users.
Analogies help people build from pre-existing mental frameworks to conceptualize new ideas, and thus make new ideas easier to understand (Bar, 2007). But the imagery those analogies rely on need to be readily available to the learners.
In this case, let's zero in on this data point: "49% of employees prefer to learn or train when necessary."
It's like being handed a roll of toilet paper and told you have to tote it around with you everywhere you go until you need to use the facilities. Your employer wants to make sure no one ever goes into the bathroom to find there is no toilet paper available when needed. You might remember to take it with you, but most of us are likely to forget it by the time crap happens. Half of all employees would want the bathroom stocked so they could just grab the toilet paper when they need it.
That's how they feel about learning too. Most want it when it's time to use it, and not before. Unless you're a bidet-only user, you were probably able to make that connection and understand why some people want training on demand while others want to carry their toilet paper-- I mean prepare with training in advance.
There are lots of ways to visualize data-- charts, graphs, pictograms, matrices, maps. These one to one representations can help make sense of complicated ideas.
Here's a really simple way to visualize "68% of employees prefer to learn or train in the workplace," which rounds up to about 7/10 people.
Check out some of these more advanced data visualizations:
Sometimes, it's not enough to just have a physical representation of the data. You need to experience it to understand it.
Let's see how experience might work for "58% of employees prefer to learn or train at their own speed."
In this case, you might bring a pilot group of employees and ask them to try learning something on the job. At the end, you could do an informal discussion, asking who preferred to keep pace with the group and who wished their could have gone at their own speed.
Honestly, there are much better use cases for using experience. This probably isn't the best one.
Check out this line game for another example.
HAVE It to Make It Stick
Facts. Figures. They're important. Sometimes, we need them to add context. But alone, data doesn't do much. It's the stories we wrap around the data that make them meaningful and useful to our learners. They help make the learning more retrievable (Mandler, 1984).
Next time, you're given a data dump by your SME, take a chance and HAVE it. Your users will thank you.
Bar, M. (2007). The proactive brain: using analogies and associations to generate predictions, Trends in Cognitive Sciences, Vol. 11, Issue 7, Pp. 280-289, https://doi.org/10.1016/j.tics.2007.05.005
Mandler, J.M. (1984). Stories, scripts, and scenes: Aspects of schema history. Psychology Press.
Bouchira, A. (2020). 68 Training Industry Statistics: 2021/2022 Data, Trends & Predictions (blog post), Research.com, https://research.com/careers/training-industry-statistics#:~:text=1%2068%25%20of%20employees%20prefer%20to%20learn%20or,employees%20prefer%20to%20learn%20or%20train%20when%20necessary
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