# Chapter

• ### Statistical literacy, reasoning, and thinking: goals, definitions, and challenges

Statistics is a discipline in its own right rather than a branch of mathematics, and the knowledge needed to solve statistical problems is likely to differ from the knowledge needed to solve mathematical problems. Therefore, a framework that characterizes creative performance in learning to reason about informal statistical inference is essential. In this paper we present an initial framework to assess creative praxis of primary school students involved in learning informal statistical inference in statistical inquiry settings. In building the suggested framework, we adapt the three common characteristics of creativity in the mathematics education literature, namely, fluency, flexibility, and novelty, to the specifics of learning statistics. We use this framework to capture creative praxis of three sixth grade students in a 60-min statistical inquiry episode. The episode analysis illustrates the strengths and limitations of the suggested framework. We finally consider briefly research and practical issues in assessing and fostering creativity in statistics learning.

• ### Reasoning about data analysis

Statistics is a discipline in its own right rather than a branch of mathematics, and the knowledge needed to solve statistical problems is likely to differ from the knowledge needed to solve mathematical problems. Therefore, a framework that characterizes creative performance in learning to reason about informal statistical inference is essential. In this paper we present an initial framework to assess creative praxis of primary school students involved in learning informal statistical inference in statistical inquiry settings. In building the suggested framework, we adapt the three common characteristics of creativity in the mathematics education literature, namely, fluency, flexibility, and novelty, to the specifics of learning statistics. We use this framework to capture creative praxis of three sixth grade students in a 60-min statistical inquiry episode. The episode analysis illustrates the strengths and limitations of the suggested framework. We finally consider briefly research and practical issues in assessing and fostering creativity in statistics learning.

• ### Software for teaching statistics

TinkerPlots are discussed in detail. Examples are provided to illustrate innovative uses of technology. In the future, these uses may also be supported by a wider range of new tools still to be developed. To summarize some of the ﬁ n dings, the role of digital technologies in statistical reasoning is metaphorically compared with travelling between data and conclusions, where these tools represent fast modes of transport. Finally, we suggest future directions for technology in research and practice of developing students’ statistical reasoning in technology-enhanced learning environments.

• ### Technology for enhancing statistical reasoning at the school level

The purpose of this chapter is to provide an updated overview of digital technologies relevant to statistics education, and to summarize what is currently known about how these new technologies can support the development of students’ statistical reasoning at the school level. A brief literature review of trends in statistics education is followed by a section on the history of technologies in statistics and statistics education. Next, an overview of various types of technological tools highlights their beneﬁ t s, purposes and limitations for developing students’ statistical reasoning. We further discuss different learning environments that capitalize on these tools with examples from research and practice. Dynamic data analysis software applications for secondary students such as Fathom and

• ### The use of stories in teaching

Good teachers have always used stories in their teaching, and often stories turn out to be more effective than arguements and explanations. I will discuss five categories of stories and their uses here.

• ### Assessing statistical knowledge, understanding, and skills

This chapter describes various methods for assessing statistical knowledge, understanding and skills. It covers traditional written forms of assessment, multiple-choice vs. open-ended questions, essays, practical work and projects, and oral assessments. It also discusses issues pertaining to the monitoring of on-going progress, groups that may be disadvantaged as the result of particular assessment methods, experiences of changing assessment methods, and the assessment of teachers.