Lesson Plan Approaches: Tasks That Motivate Students to Think

In recent years, it has been increasingly necessary for citizens to understand real life statistical dataReal life statistical data—an ability that is rarely taught in schools, where the majority of tasks in statistics classes contain fictional data without context and make no demands on students to explore or explain. Since most real-world phenomena are multivariate (See Chap. 2 ), there is a need to develop students’ abilities dealing with complex data and stories they encounter in the media, in order to help prepare them for informed citizenship. The ProCivicStat project has developed materials to support teaching and learning, in the form of detailed lesson plansLesson plans; a large repository of resources ( http://iase-web.org/islp/pcs/ ) (in several languages) is freely available. This chapter describes our approach to the development of teaching resourcesTeaching resources. It introduces our storytellingStorytelling approachStorytelling approach in lesson plans, where we use real data in contextData in context to encourage students to explore and understand complex data, produce narrative accounts, and often make recommendations about appropriate social actions. The structure of this chapter is as follows: we start with a brief introduction on problems in most tasks commonly encountered in statistics education, and the need for real dataReal data in statistics teaching (Sect. 7.1), followed by the presentation of the milestones that are important for creation of lesson plansLesson plans (Sect. 7.2), and after that we address the use of real dataReal data and our storytellingStorytelling approachStorytelling approach (Sect. 7.3). In Sect. 7.4 we talk briefly about empowering teachers (Sect. 7.4) and describe the teachers’ version of the lesson plan (Sect. 7.5). In Sect. 7.6 we present the guidelines for designing student activities, then proceed with an excerpt of a lesson planLesson plan to exemplify products of the proposed guidelines (Sect. 7.7). We then highlight the visualization tools that help promote the data exploration step (Sect. 7.8), and finish with a conclusion (Sect. 7.9).