Technological tools enable students to present and organize data easily and immediately through interactive and dynamic graphical representations. Thoughtful use of these tools can shift the focus away from procedural end skills (such as performing calculations or manually drawing graphs) and allow students to concentrate on and deepen their analysis of data and graphical representations.
The use of graphical representations of data can promote students’ understanding of the phenomenon they are studying, motivate them to continue inquiry out of interest, and encourage a focus on higher-order thinking skills. Tools that allow quick and simple transitions between different graphical representations encourage reflection on the differences between representations and the distinct interpretations they foreground. Tools that immediately display changes in data patterns or trends in graphical representations following the addition of new data encourage continued and deeper investigation of the phenomenon.
In citizen science, this principle has particular added value, as projects of this type often involve enriching large-scale data repositories. Data collected by students in a project are integrated with existing datasets and contribute to their enrichment. Dynamic visualization tools allow students to see how the data they collected contribute to (alter or influence) the existing representation. For example, in the “Jellyfish in the Nation” project, students report the presence of jellyfish along different beaches. Reports are submitted via an application and immediately appear on an interactive online map that displays all reports across the country. The technological tool CODAP, which is a dynamic tool for data exploration and visual representation, enables students to organize data as they wish across different graphical representations in an immediate and simple manner, to perform various manipulations on the data, and to explore and compare different graphical representations.
Deepening and Expansion ▼
Advantages of technology
According to Biehler, Ben-Zvi, Bakker, and Makar (2013), technology has played a historical and significant role in the development of statistics education over the years. The integration of technology has led to changes in the teaching of statistics, both in terms of content and in terms of pedagogical approaches to learning and teaching statistics. Various technological tools enable sophisticated ways of presenting and organizing data. Thoughtful use of these tools can shift the focus away from procedural skills (such as performing calculations and drawing graphs) and allow students to focus on analyzing data and graphical representations. Such use may open opportunities for developing more sophisticated data analysis methods grounded in conceptual understanding of different statistical ideas.
One of the major approaches that emerged following technological developments is Exploratory Data Analysis. This approach, first developed by Tukey (1977), places at the center of statistics the practices of collecting, organizing, describing, and analyzing data. It emphasizes visual tools and representations, some of them novel, and the use of technology for interpretation, analysis, and inference. The emphasis in this approach is on practices such as identifying patterns and trends in data, examining the purposes and reasons for data collection prior to the inquiry itself, and thinking in terms of generating hypotheses, investigating them, and corroborating them.
Technological tools for data analysis and the TinkerPlots software
Biehler, Ben-Zvi, Bakker, and Makar (2013) provide a review of different types of technological tools for teaching statistics. Among these are several tools used for analyzing and representing learners’ data:
Software packages
Computer programs designed for statistical data analysis. In most cases, these programs are intended for professional use, though they are also used in higher education. Examples include SPSS, SAS, and R.
- Spreadsheets:
Accessible and user-friendly tools. In schools, these tools are used by students to learn about data organization and representation. The range of graphs available is relatively limited by default, and it is not easy to integrate diverse statistical measures. Examples include Excel. - Graphing calculators:
Tools for learning data analysis and inquiry (primarily in higher education) that enable the performance of simple and complex statistical calculations and procedures. These tools often have limitations, such as outputs displayed without variable names or without scales. An example is TI-Nspire. - Educational software:
Software developed specifically for the learning and teaching of statistics. These are typically flexible and dynamic tools designed to allow students to explore and learn abstract statistical ideas and processes. Such tools are often accompanied by data repositories, instructional videos, and collections of activities. Examples include TinkerPlots and CODAP.
The TinkerPlots software (Konold, 2007)
TinkerPlots is a data analysis tool that also includes tools for creating simulations. It is designed to support the development of statistical thinking among students in grades 4–8 and enables open-ended exploration and experimentation using diverse visual representations and statistical supports. With TinkerPlots, data can be organized in simple and varied ways in order to identify patterns, trends, and characteristics of the investigated attributes. In this way, and with appropriate guidance, students can develop a conceptual understanding of what statistics is and how it can be used. Unlike other data analysis support tools, TinkerPlots was developed specifically for students and was therefore designed according to a “bottom-up” approach, that is, in alignment with students’ intuitive ways of thinking about mathematical and scientific topics. According to this approach, curricula and learning tools are built around students’ starting points rather than the desired end points.
References ▼
Biehler, R., Ben-Zvi, D., Bakker, A., & Makar, K. (2013). Technology for enhancing statistical reasoning at the school le In M. A. Clements, A. Bishop, C. Keitel, J. Kilpatrick, and F. Leung (Eds.), Third International Handbook of Mathematics Education (pp. 643-690). Springer.
Kali, Y., (2006). Collaborative knowledge-building using the Design Principles Database. International Journal of Computer-Supported Collaborative Learning, 1(2), 187-201.
Konold, C. (2007). Designing a data analysis tool for learners. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 267-291). New York: Taylor and Francis.
Tukey, J. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley.
