A new school curriculum, with substantial statistics content at all levels, is currently being phased in throughout South Africa. This paper focuses on a government roll-out plan that aims to upgrade the knowledge of in-service teachers in order to empower them to successfully engage with the statistics content of the new school syllabus.
We present the foundations of a professional development program supported by the European Union (COMENIUS Project 226573-CP-1-2005, developed from December 2005 to December 2008), whose objective is to propose professional development strategies that foster the integration of the teaching and learning of statistical reasoning in European schools. The intention of the program is to promote professional development through cross-cultural collaboration between teachers of different European countries. To this end, an online professional learning environment has been designed. We present the referents that allow us to interpret the teachers' reasoning and to understand how their intervention in the teaching and learning processes evolves.
The EU-funded project EarlyStatistics aims to enhance the teaching and learning of early statistical reasoning in European schools by utilizing distance education to offer high-quality professional development experiences to teachers across Europe. The project consortium has developed and is currently pilot testing an online professional development course in statistics education targeting elementary and lower secondary school European teachers. The article provides an overview of the EarlyStatistics course design. It describes the pedagogical and didactical approach underlying EarlyStatistics and the course content and structure. It also outlines the quality assurance processes used in the project to avoid quality failures and the evaluation processes employed to assess the course effectiveness in achieving its objectives.
We explore the tensions between cooperation and competition in the context of improving<br>the content, delivery and penetration of statistics education, and improving the health of statistics<br>groups in universities. University education has many more parallels with business than most of<br>us appreciate. Our environments are increasingly competitive on many, many levels. Competing<br>well is essential for us to prosper, certainly. But it is sometimes necessary even for survival. It is<br>suicidal for us just to be warm, fuzzy, nurturing educators who expect the world to appreciate our<br>essential worth and reward us accordingly. We have also to be entrepreneurs and battlefield<br>strategists. We explore models for increasing the numbers of students studying statistics,<br>improving their educational experiences, and increasing the usefulness of the statistics education<br>they receive. Along the way we develop sets of principles to guide our planning and operations.
The term `cartoons' usually suggests humorous, animated drawings, along the lines of Mickey<br>Mouse or Charlie Brown. However, a much older use of the word refers to the prototypes or trial<br>drawings of artistic masters such as Michelangelo, in preparation for the finished work to follow.<br>In a broad sense, graphical insight into statistical ideas connects with both these meanings; the<br>aim is to give students a means of exploring concepts until they are comfortable with their roles,<br>while the ability to animate adds an extra dimension which can often spark additional interest and<br>which can sometimes raise a welcome smile.<br>This talk will discuss some of the ways on which animated graphics can help in the understanding<br>of statistical ideas at elementary, intermediate and advanced levels. The `rpanel' package for R<br>will be used as a vehicle but other systems will also be mentioned. Over the years there has been<br>considerable focus on illustrations of elementary statistical concepts and there are many good<br>examples at that level. However, the scope for tools addressing more advanced topics, such as<br>likelihood and spatial sampling, will also be discussed.<br>The advent of R as a standard computing environment in statistics, with increasing connectivity to<br>other systems, makes it entirely feasible for lecturers to construct their own cartoons, rather than<br>simply use those designed by others. The talk will argue for the importance of this mode of use.
In this paper, we summarise several components of our recent research into students' conceptions<br>of statistics, their learning of statistics, our teaching of statistics, and their perceptions of their<br>future professional work. We have obtained this information on the basis of phenomenographic<br>analyses of several series of interviews with students studying statistics, both as statistics majors<br>and as service students. In each of these cases, the broadest views relate in some way to personal<br>connection, growth and change - in other words, they contain a strong ontological component<br>above and beyond the standard epistemological component of learning. We discuss the<br>importance of personal change in becoming a statistician - or an informed user of statistics - and<br>investigate the pedagogical conditions under which such change is likely to occur.
The motivational value for students of problem-based immersion in the process of data collection,<br>data analysis and interpretation, is accepted by many. However, the culture of instruction<br>through technique-based courses is still used at the tertiary level in many universities. The<br>coverage of topics seems to trump guidance through the process of data analysis. In this paper, I<br>suggest how to complement a problem-based experiential presentation of statistical methods with<br>a presentation of the abstract structures necessary for future applications. A series of problembased<br>courses might fail to highlight the general and transferable concepts and principles that<br>help to bring coherence to the toolbox of statistical techniques. To overcome this shortcoming<br>one can present the logical structure - that is definitions, strategies, theoretical frameworks and<br>justifications - to unify the collection of problem-specific methods, but only after extensive<br>immersion in practical problems. Once students have experienced the effectiveness of the<br>practical statistical approach, they may be better prepared to absorb the abstract generalizations.
Analogical thinking is a powerful cognitive tool that leverages knowledge and<br>understanding of familiar ideas and relationships to form knowledge and understanding in a new<br>setting. For students approaching their first statistics class, fear of the unknown can be a major<br>factor in slowing and even stopping learning. Yet many statistical ideas have their roots in<br>thinking with which students are already familiar. Knowing this fact, and how to exploit it<br>through the use of analogy gives us a decisive advantage in the battle for hearts and minds of<br>students who do not yet know how much they need statistics in their lives. I will describe analogy<br>as a tool for teaching statistics, my experiences with its use, and many examples of analogies I<br>have invented, borrowed, stolen, lost then rediscovered, and otherwise acquired.
Statistics education should include teaching students statistical technological literacy,<br>which I define to be the ability of students to use and criticize technology in the context of doing<br>statistics. Technological literacy is a very important component of the education of data<br>scientists, particularly because Statistics' unique relationship with technology means that changes<br>in technology affect not only how we practice our profession, but the objects we study. After<br>discussing and illustrating aspects of this relationship, this paper reports on the development of a<br>new journal, Technology Innovations in Statistics Education. The journal was founded with the<br>intent of encouraging more research and discussion into the role that technology plays in statistics<br>education.
Although project work involving analysis and interpretation of real data is important when<br>students are learning statistics, there is an important role for short exercises to help learn specific<br>statistical skills. Computer-based exercises can be much richer than exercises in paper-based<br>textbooks but existing resources do not make full use of the medium. The format can involve<br>multiple-choice, numerical answers, interaction with diagrams (such as sketching a histogram) or<br>a combination of these, possibly in sequence. The exercise can analyse the student response and<br>give helpful hints and feedback about different types of incorrect answer. Random generation of<br>similar questions in an exercise can allow repeated attempts until skills are mastered. Some<br>principles are given for the design of computer-based exercises and a set of nine exercises about<br>normal distributions is described.