Use of Data Visualisation in the Teaching of Statistics: A New Zealand Perspective

Abstract

For many years, students have been taught to visualise data by drawing graphs. Recently, there has been a growing trend to teach statistics, particularly statistical concepts, using interactive and dynamic visualisation tools. Free down-loadable teaching and simulation software designed specifically for schools, and more general data visualisation tools are increasingly being used in New Zealand classrooms. This paper discusses four examples: the use of GenStat for Teaching and Learning Schools and Undergraduate (GTL); Auckland University’s iNZight and VIT (Visual Inference Tools) for teaching bootstrapping and randomisation; the CAST e-books, and the use of data visualisation tools to assist learning concepts in official statistics. All these tools are publically available and several are already being used internationally. First published November 2014 at Statistics Education Research Journal Archives

Data Scientists’ Epistemic Thinking for Creating and Interpreting Visualizations

“…The new research area, data science, was first introduced by Cleveland (2001) in an action plan for the field of statistics, which described how the technical areas of statistics needed to be redesigned for practicing analysts to learn from data by using various tools that have a direct benefit while understanding statistical theories that have an indirect benefit. In recent years, technological advances have had a universal impact on the ability of various industries and research fields to collect, store, visualize, and analyze large amounts of data (Eilam, 2015;Forbes et al, 2014). As a result, visualization software tools have developed over the past ten years to present complex data and information in a more succinct way so as to foster interpretation and discovery of new relationships or patterns (Forbes et al, 2014;Keim et al, 2010;Mirel et al, 2016).…”

“…In recent years, technological advances have had a universal impact on the ability of various industries and research fields to collect, store, visualize, and analyze large amounts of data (Eilam, 2015;Forbes et al, 2014). As a result, visualization software tools have developed over the past ten years to present complex data and information in a more succinct way so as to foster interpretation and discovery of new relationships or patterns (Forbes et al, 2014;Keim et al, 2010;Mirel et al, 2016). The use of visualization methods and techniques to make sense of large datasets has become an essential way to represent and interpret information (Figueiras, 2013).…”

“…Students' ability to understand and identify the variability in data can be improved through seeing and interacting with different graphical displays and seeing visual comparisons (Cooper & Shore, 2010). In addition, use of pictorial visualizations helps students grasp concepts like statistical inference (Forbes et al, 2014). The understanding of variability is a part of statistical thinking.…”

How can histograms be useful for introducing continuous probability distributions?

“…In this case we chose the GeoGebra software, most frequently used by French teachers. We think that the use of software is of interest in teaching statistics (Biehler 1991;Ben-Zvi 2000;Forbes et al 2014) and the transition toward continuous probability as well. Similar research has been published for normal distribution (Batanero et al 1999).…”

Computes: Development of an Instrument to Measure Introductory Statistics Instructors’ Emphasis on Computational Practices

“…For example, the Pro Civic Stat project in Europe has focused on promoting statistical literacy by using real world social contexts that are often multivariate (Gal et al, 2016;Schiller & Engel, 2016). Similarly, educators in New Zealand have integrated computation and authentic multivariate data and visualization within curriculum materials at all levels of education using thoughtfully designed software such as INZight and VIT (Forbes, 2014;Forbes et al, 2014). Other global efforts include the International Data Science in Schools Project (IDSSP Curriculum Team, 2019), a project to support and promote the teaching of data science in schools.…”