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Success messages: best practices
Success messages: best practices

Some tips and tricks for writing insightful success messages.

Amy Peterson avatar
Written by Amy Peterson
Updated over a week ago

Once a learner has successfully completed an interactive DataCamp exercise, they receive a "success message." Success messages not only allow you the opportunity to congratulate a learner on their success but are an excellent opportunity to wrap up an exercise by pointing out how the learning objective was achieved using insights from the exercises. Ideally, the success message should directly relate to what is written in the context and should provide an interpretation of the results obtained upon submitting a correct solution. Think of success messages as part two of the context section. Read on for some tips on how to write informative, effective, and insightful success messages.

Screenshot of coding exercise with success message highlighted

How long should the success message be?

The message should consist of a few words of praise followed by one or two sentences of informative insight. Do not write an essay here!

Be creative about the praise

"Great!" is not as great as you think! Having a bit of variation is good. Try using rhymes or alliteration, and relating the praise to the contents of the exercise.

Draw attention to the results

For exercises that get the learners to calculate results or draw a plot, it's very easy for the learner to complete the exercise and not actually look at what they've just done. A success message that highlights interesting results can be used to get the learners to go back and look at what they just did, and why it is meaningful.

Motivate the next exercise

By highlighting a problem with something in the exercise, you can motivate the contents of the next exercise (where you solve that problem).


From Working with parquet files in "Introduction to Spark in R using sparklyr." This has a creative piece of praise, followed by a heuristic that is useful to know, but hard to demonstrate directly in the exercise.

  • "Smooth as some parquet flooring! Reading and writing Parquet files is much quicker than reading and writing CSV files, and typically faster than using copy_to()."

From Drawing your first plot in "Introduction to Data Visualization with ggplot2." This begins with (almost) alliterative praise, followed by a comment that draws the learner's attention to the plot that they just drew. It mentions a problem that motivates the following exercise.

  • "Phenomenal plotting! Notice that ggplot2 treats cyl as a continuous variable. You get a plot, but it's not quite right, because it gives the impression that there is such a thing as a 5 or 7-cylinder car, which there is not."

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