The Discipline of Statistics Education (original) (raw)

The Proceedings of the 12th International Congress on Mathematical Education, 2015

Being able to provide sound evidence-based arguments and critically evaluate data-based claims are important skills that all citizens should have. It is not surprising therefore that the study of statistics at all educational levels is gaining more students and drawing more attention than it has in the past. The study of statistics provides students with tools, ideas and dispositions to use in order to react intelligently to information in the world around them. Reflecting this need to improve students' ability to think statistically, statistical literacy and reasoning are becoming part of the mainstream school and university curriculum in many countries. As a consequence, statistics education is a growing and becoming an exciting field of research and development. Statistics at school level is usually taught in the mathematics classroom in connection with learning probability. Topic Study Group 12 (TSG-12) included probabilistic aspects in learning statistics, whereas research with a specific focus on learning probability was discussed in TSG-11 of ICME-12.

Introducing the emerging discipline of statistics education

Increasing attention has been given over the last decade by the statistics, mathematics and science education communities to the development of statistical literacy and numeracy skills of all citizens and the enhancement of statistics education at all levels. This paper introduces the emerging discipline of statistics education and considers its role in the development of these important skills. The paper begins with information on the growing importance of statistics in today's society, schools and colleges, summarizes unique challenges students face as they learn statistics, and makes a case for the importance of collaboration between mathematicians and statisticians in preparing teachers to teach students how to understand and reason about data. We discuss the differences and interrelations between statistics and mathematics, recognizing that mathematics is the discipline that has traditionally included instruction in statistics. We conclude with an argument that statistics should be viewed as a bridge between mathematics and science and should be taught in both disciplines.

Approaches to Broadening the Statistics Curricula

Recently, there has been a lot of discussion about what a statistics curriculum should contain, and which elements are important for different types of students. For the most part, attention has been understandably focused on the introductory statistics course. This course services thousands of students who take only one statistics course. In the United States, the course typically fulfills a general education requirement of the university or a degree program. There has also been considerable activity regarding the use of computers to present statistical concepts and to leverage the Web and course management software to interact with students. Recently, there has been debate as to whether statisticians should make ambitious changes using resampling, the bootstrap, and simulation in place of the more traditional mathematical topics that are seen as the fundamentals or origins of the field (Cobb, 2007). It is unclear that we are achieving the goals of basic statistical literacy by focusing on formulae or even by concentrating almost exclusively on methodology. Instead, we believe the field and students would be significantly better served by showing the challenges and applicability of statistics to everyday life, policy, and scientific decision making in many contexts, and by teaching students how to think statistically and creatively. In contrast to the activity at the introductory level, there has been much less attention paid to updating the statistics curricula for other categories of students. While smaller in number, these students—undergraduate majors and minors, masters, and doctoral students—are very important, as they are the ones who will use statistics to further the field and improve the quality of research. Other disciplines (e.g., biology, geo graphy, and political and social sciences) are increasingly appreciating the importance of statistics and including statistical material in their curricula. Further, statistics has become a broader subject and field. However, the statistics curricula at these levelshave not changed much past the introductory courses. Students taking courses for just 2 years may not see any modern statistical methods, leading them to a view that the important statistical ideas have all been developed. More importantly, few students will see how these methods are really used, and even fewer will know at the end of their studies what a statistician actually does. This is because statisticians very rarely attempt to teach this; instead, they labor over the details of various methodologies. The statistics curricula are based on presenting an intellectual infrastructure in order to understand the statistical method. This has significant consequences for improved quantitative literacy. As the practice of science and statistics research continues to change, its perspective and attitudes must also change so as to realize the field's potential and maximize the important influence that statistical thinking has on scientific endeavors. To a large extent, this means learning from the past and challenging the status quo. Instead of teaching the same concepts with varying degrees of mathematical rigor, statisticians need to address what is missing from the curricula. In our work, we look at what statistics students might do and howstatistics programs could change to allow graduates to attain their potential.

This paper discusses the objectives that would be appropriate for statistics classes for students who are not majoring in statistics, evaluation, or quantitative research design. These "non-majors" should be able to choose appropriate analytical methods for specific sets of data based on the research question and the nature of the data, and they should be able to interpret the results of data analyses in light of the research question that was proposed. Non-majors should be able to choose from and to interpret the results from these classes of statistical procedures: (1) descriptive statistics (measures of central tendency and measures of variations); (2) measures of relative standing; (3) measures of association (bivariate correlation and regression and multiple correlation and regression); (4) simple cases of hypothesis testing (t-tests, analysis of variance, and analysis of covariance); and (5) multivariate techniques (multivariate analysis of variance, factor analysis, discriminant function analysis, and canonical correlation and regression). Nonmajors also should be able to use statistical analysis computer packages, with the depth of learning determined by the needs of the student. (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.

Data analysis or how high school students “read” statistics

Statistics Education and the Communication of Statistics International Association for Statistical Education Satellite Conference

In most countries, statistics are included in the mathematics curriculum and taught by mathematics teachers. This leads to students learning the elements of statistical concepts as mathematical and to more emphasis placed on being able to compute different measures (e.g. mean, median, standard deviation) rather than their meaning and use. Moreover, in Quebec, the high school curriculum favours a scattered presentation of statistical concepts: tables and simple graphical representations are seen in the first year; averages, medians and histograms in the third; position measures in the fourth and some aspects of correlation and standard deviation are seen in the fifth. Some elements of probability are seen in the second year. But “statistics requires a different kind of thinking” (Cobb & Moore, 1997). Is it possible by making students compute statistical measures to foster the development of statistical thinking and prepare to draw conclusions from different data sets - all important ...

How Students Learn Statistics

International Statistical Review / Revue Internationale de Statistique, 1995

Research in the areas of psychology, statistical education, and mathematics education is reviewed and the results applied to the teaching of college-level statistics courses. The argument is made that statistics educators need to determine what it is they really want students to learn, to modify their teaching according to suggestions from the research literature, and to use assessment to determine if their teaching is effective and if students are developing statistical understanding and competence.

Getting Real Statistics into all Curriculum Subject Areas: Can Technology Make this a Reality?

Technology Innovations in Statistics Education, 2013

Technology has revolutionised society and it has revolutionised the way in which statistics, as a professional discipline, is done. The collection of data is growing exponentially both in relation to the quantity of data assembled on any particular measure and also in relation to the range of topics, and the measures, on which data is collected. Accessing data has become much simpler, and tools for exploring, manipulating and representing that data visually have multiplied, both in commercially available software and open-source freeware. However, the curriculum in schools in the UK is constrained by important factors which restrict the use of technology in assessment. The statistics curriculum is largely dull and does not address the core issues of most relevance in statistics today. Here, we explore ways in which technology can enhance the teaching of subjects in which statistics are used, and also the teaching of statistics within mathematics. INTRODUCTION: The Royal Statistical Society launched GETSTATS in 2010, a 10 year statistical literacy programme which addresses statistical literacy across the full spectrum of society-including formal education across many disciplines. They initiated a debate looking at the mathematical and statistical needs of all subject areas at school level and at undergraduate programme level, with an eye to preparation for involvement in research. This includes two separate strands looking at the needs and current state of provision within humanities and social sciences and separately within science, engineering and technology (STEM) subjects. This initiative is partly in response to a number of major reports (ACME (2011 a, b), Hodgen, Pepper, Sturman and Ruddock (2010), Vorderman, Budd, Dunne, Hart and Porkess (2011)) which identify the UK as lagging well behind many other countries in terms of the number of 16-19 year old students who are studying some mathematics, with recommendations that have been accepted by the current government that all students should study some mathematics, preuniversity. However, A-level mathematics (a specialist 'gatekeeper' course for STEM subjects for 16-18 year olds) is currently taken by nearly 80,000 candidates but is not an appropriate qualification for most of the rest of the cohort. Key questions arise: what should they study? and, crucially, who is going to teach them (since there is already a severe shortage of well-qualified mathematics teachers to deliver the current courses)? Holmes (2000) analysed the statistical requirements embedded in all subjects which are available in the English National Curriculum; Porkess (2012) has undertaken an updating of that work. His report analyses the subject demands in terms of 4 components of statistical work: A. Problem analysis B. Data collection C. Data presentation D. Data analysis Statistics is currently taught within mathematics. In primary school, (ages 5-11 years), components A, B and C are treated coherently at an appropriately simple level, but at GCSE (the high stakes assessment at the end of compulsory schooling, age 16) only component C has any prominence and at A-level (at age 18 / 19) only components C and D are involved. Psychology, Geography and Biology address all 4 components, yet anecdotal evidence about statistics within those subjects is often of it being treated as a black box of techniques, rather than as a way of thinking about the world. One consequence of the curriculum specialisation possible in early secondary school in the UK is that pupils seem to split into two groups which almost seem to inhabit non-overlapping universesthe worlds of social science and of mathematics and statistics. The vast majority of students never encounter real social problems as substantial contexts in which they work to develop

The Discipline of Statistics Education (original) (raw)

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