1. Defining learning objectives
In this lesson, we'll discuss how to build your course around learning objectives and cement these objectives using real-world scenarios.
2. Clear learning objectives
The first step in building out a lesson is to write clear learning objectives. A learning objective describes a measurable outcome that a learner will achieve by the end of a lesson. They usually begin with "Learner will be able to" and then describe what is expected of the learner. Writing clear learning objectives helps you properly scope your lesson. If you know exactly what content you want to cover, shaping this information into learning objectives will help determine the scope of the lesson, particularly if it is too much or too little for a DataCamp lesson.
3. Measurable learning objectives
Learning objectives should be measurable. This means that you can write an assessment that proves whether or not the objective has been met. For example, we can test if a learner can define the word "variable" using a Multiple Choice exercise. We can measure if a learner can create a variable using a Coding exercise. Vague verbs like "know" and "understand" don't tell us what exercises are necessary to measure the objective.
4. Specific learning objectives
Learning objectives should be specific. Specificity keeps our lessons engaging. It also helps DataCamp understand exactly what you want to teach in your course. Specific learning objectives help us spot missing concepts and check for overlap with existing courses.
For example, rather than saying "Learner will perform sentiment analysis," a better objective will tell us what tools or types of classification will be taught.
5. Learning objectives solve a problem
Learning objectives answer the "Why" that you learned about in the previous lesson by providing real-world examples.
Adding a "why" to learning objectives makes them more interesting to learners. For example, "Learner will be able to create histograms to compare datasets with similar means, but different distributions" is more engaging than "Learner will be able to create histograms".
6. Learning by doing
As you build your course, you'll quickly see that DataCamp is all about learning by doing. That's why we built an interactive platform that lets learners write code as soon as they learn a new concept.
In order to engage learners and fulfill our mission, we want to get learners coding as quickly and as often as possible. We prefer to have learners build the models and analyses that they're learning about rather than multiple choice questions about the topic.
When building interactive exercises, make them as realistic as possible. If there is a ready-to-use library that runs a specific analysis or model, use that rather than re-building the model from scratch.
7. Expertise and industry insights
Interactive exercises and slides should be built around engaging, real-world scenarios. This means using real datasets, rather than generating random numbers with no meaning.
8. Expertise and industry insights
Even when teaching simple concepts, like defining variables, it's important to have a real-world element. For example, having variable names that represent real pieces of data like the height of Mount Everest, is better than using generic names like "foo" and "bar." This is not only helpful for novices to understand your code but is also good coding practice!
9. Summary
So remember, when creating your lessons, write measurable learning objectives, encourage learning by doing, and incorporate real-world experience into your videos and exercises.
10. Let's practice!
Let's practice these new skills with a few exercises!