One benefit of the technology is data visualisation. Ridgway et al (2017) stated the practical and theoretical implications of the data visualisation as follows:
At the practical level, data visualisations can facilitate exploratory learning and can be used directly to reshape teaching. At a conceptual level, understanding data visualisations has become an important element of statistical literacy. Students need experiences working with and critiquing novel visualisations. If we are to promote statistical literacy in the broader community, we need a better understanding of the cognitive processes involved in working with complex visual displays.
For example, GeoGebra has a page for Data handling and statistics. Common Online Data Analysis Platform (CODAP) is a free web-based data visualisation tool. The website provides several ready-made learning environments, but we can easily create our own files by either inputting data manually or uploading text/csv files.
When a data set is imported to CODAP, then we can create dynamic graphs by clicking ‘Graph’ icon. Each data can be dragged and dropped onto the graph, and it is possible to manipulate data visually in order to explore various relationships between data.
It is possible to display values such as means, and draw a least squares line or a box plot on the graph. The data can be treated as categorical, numerical, date, qualitative and boundary.
By inputting longitudes/latitudes, it is possible to link between data and map. The figure below is an example of the air pollution levels measured in various streets in Exeter on CODAP.
Click to enlarge
It is expected that CODAP can provide opportunities for statistical reasoning and modelling with various real data (see our case studies).
Also video tutorials are available from here.
Ridgway, J., Nicholson, J., Campos, P., & Teixeira, S. (2017). Tools for visualising data: A review. IASE Satellite Conference.