Significance. Null hypotheses. P-values. Normal distributions. Tricky stuff!
Wouldn't it be great if it was possible to *visualize* these, and really understand them?
Our analysis process does let the student perform a hypothesis test, the result of which is the experimental significance.
But it's built up as a step-by-step tutorial, which using interactive animations provides the student with the challenges and information that can lead to a real understanding of the process.
The "P-slider", one of the widgets they'll play with
DataClassroom is built on the following principles:
Get the students thinking
Throughout the process, the student is encouraged to consider what they are doing. What is the purpose of an analysis? What would the meaning of any result actually be?
The process is broken up into smaller chunks, each of which can be understood by the student. This builds confidence in their own understanding.
Show and tell
We use visualizations to provide the intuitive side to the understanding. But we also provide the math, which they can use as a reference and connect to the visualized picture.
Let the computer do the dull stuff
To save the student's attention and energy for the important thinking, the computer does any tedious repetitive calculations. That's why we have computers, right?
Connect accessible wording and technical language
Instructions and explanations should be understandable before you have the full vocabulary. Provide the correct technical terminology as a side-show, so it is available when needed, but doesn't obstruct initial understanding.
Make the practical stuff easy
Students need to make lab reports, turn in assignments, and so on. Make it easy to get data in and out of the tool, so they spend their time productively.