Method understanding charts
How to read and understand trend data
- to critically use depictions of risk (charts).
Why is it relevant to improve consumers' risk literacy?
The aim is for the consumer to be able to make better decisions. For many of these decisions, statistical information is available in the form of graphical risk charts. Charts make it easier to identify relationships and patterns that would remain hidden in tables. Since comprehending graphic depictions is easier for many people than reading tables, charts can also be a gateway for manipulation. Actors who want consumers to make a certain decision, do so by designing graphic information in a certain way. Consumers must therefore be able to become aware of manipulations. However, the ability to critically reflect on information from a standard graphical representation that are relevant for a risk decision needs to be practiced (as it is already the case with mathematics lessons in schools).
Why is it problematic to improve risk literacy regarding graphic depictions?
- conveying how data is to be interpreted, aside from reading,
- conveying how manipulations can be detected, aside from reading,
- sparking interest to engage with these learning contents.
The interest of many learners in dealing with statistics can be activated by the performance motive, i.e. the progress that could be achieved. This can be illustrated by an applicable increase in competence. Accordingly, the learning visualisation is implemented on the basis of a relevant consumer example: Line charts with financial data as data source for common decisions with high financial stakes.
In this learning visualisation, test tasks are used. Their difficulty increases gradually. It follows the steps of learning how to read off a chart, how to interpret it, and how to manipulate it.
(1) Point values, which require an understanding of axes, their labels and the axis ticks in relation to the data and their legend, are read off the chart.
(2) Trends that can rise, fall or express volatility are interpreted. Here, the complete line paths with regard to the axes, their labels and the axis ticks as well as the legend are to be compared with each other.
(3) Manipulation should be learned in order to be able to examine charts for certain strategies of manipulation in the real world. Here not only axes, labels and axis ticks must be set in relation to the data and their legend. It is also necessary to understand their interaction and to achieve a given goal by examining the data section and magnification factor on two axes.
- Barth, R. (2018). Möglichkeiten der Nutzung von Game Design Prinzipien in der Erwachsenenbildung. digital. innovativ|# digiPH, 109–118.
- Eitel, A., & Kühl, T. (2019). Harmful or helpful to learning? The impact of seductive details on learning and instruction. Applied Cognitive Psychology, 33(1), 3–8.
- Sailer, M., Hense, J., Mandl, J., & Klevers, M. (2014). Psychological perspectives on motivation through gamification. Interaction Design and Architecture Journal, (19), 28–37.
Option 1: You can embed the given visualisation
<iframe frameborder="0" height="650px" src="https://static.risikoatlas.de/modules/understanding-graphs/" width="1024px"></iframe>
What do I need this visualisation?
If you want to learn something. The purpose of this learning visualisation is to practice your ability to critically use graphic depictions of risk.
What does the visualisation show?
The visualisation shows three investment funds. Specifically, it shows how their value (y-axis) has changed over the quarters of several years (x-axis). Learner are guided by multiple-choice questions. They always receive immediate feedback on what was correct and what was wrong, and thus work linearly through the complete visualisation. In addition, social feedback is always given, i.e. how many other learners were able to correctly answer a certain question.
What is the quality of the data?
The numbers are published and can be found on all financial websites that list investment funds. For market participants, the quality of the published numbers is considered to be factually correct, i.e. very high.
Empirical evaluation with consumers