Last week I covered what learning objectives are and how to write them using Bloom's Taxonomy. Today I want to share what they look like in the wild so you can see how we reverse engineer them.

In the course of doing some client work, I came across Cloudflare's "What is Mutual TLS?" article on their learning center.

On the left side of the article Cloudflare has clearly defined learning objectives, copied below:

After reading this article you will be able to:

- Explain how mutual TLS (mTLS) works
- Understand the difference between mutual TLS and regular TLS
- Illustrate how mTLS stops attacks

Now with the understanding of Bloom's Taxonomy, it should be straightforward to reverse engineer these objectives.

The first two are at the Understand level denoted by the two action words: "explain" and "understand". Understanding can be taught through the written word pretty easily.

But what about "illustrate"? It's different than simply understanding a concept, illustrate denotes some application – as in, you will be able to see how mTLS stops attacks and be able to illustrate it yourself. This is demonstrating knowledge so I would classify it under the Apply taxonomy level, and corresponds with the action word "sketch."

In fact, if you take a look at the article, there are actually diagrams that illustrate how mTLS stops attacks. The Apply taxonomy level moves content in the direction of application and demonstration – beyond just words on a screen to incorporate visuals or code.

Do you notice something else?

It was the last learning objective. Why? Because understanding must come first. If I asked you to draw a diagram of how your car starts, you couldn't if you didn't already understand the components that made up the process of starting your car.

How do you come up with learning objectives for a new experience? For the rest of this week, I'll focus on answering that by re-thinking an existing real-world learning experience for our open source project.

Have a lovely day,
Kamran

What learning objectives for technical articles look like in the wild

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