Developing better diagrams for problem solving

This post about an approach to developing better diagrams for problem solving from Barbara Tversky’s book: Minds in Motion.

I’ve gone back to one of the original papers for details (Kessel and Tversky here). The principal they use is an empirical ‘design lots and try them out’ approach they call Production, Performance and Preference. The authors have used this approach to design bettermaps for solving problems and flat-pack assembly instructions (apparently Lego and Ikea graphics score very highly).


Creating Visual Explanations Improves Learning Eliza Bobek & Barbara Tversky (5)
Production, Performance, and Preference – the three stages of empirically developing better graphics.



In this study, the authors asked one group of undergraduates to produce a representation of the following data type:

Suppose you need to keep track of where your friends are during the day so that you can answer questions like:

  • Were Alex and David together downtown? Or,
  • how many people were at the gym in the afternoon?
  • Did Justin go to the library before he went to the

Here is the information you will have to keep track of:

  • People: Alex, David, Justin, Sammy
  • Places: dorm, library, downtown, gym
  • Times: morning, noon, afternoon, night

Participants produced several types of diagrams – the majority as a form of table or matrix.

Screenshot 2019-10-02 at 13.10.28
Example designs from the ‘produce’ stage (copied from the Kessel/Tversky paper)


The authors proceeded to create a bank of problems which could be solved using the diagrams. They gave the next group of participants the problems with different diagram designs. They recorded the time taken to solve the problems with each type of diagram.


Finally, Kessel and Tversky asked participants which type of graphic they preferred.

My Conclusions

We use diagrams and charts a lot in teaching. It seems to me that we can reduce extraneous cognitive load by using better diagrams. This model of diagram development shows how we can improve designs empirically. In my next post, I’m hoping to report on a version trialled by colleagues on twitter. Watch this space…




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