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Reasoning about uncertainty: learning and teaching informal inferentia l reasoning Andy Zieffler and Elizabeth Fry eds. Ben-Zvi, D. The challenge of developing statistical literacy, reasoning, and thinking.
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- The Challenge of Developing Statistical Literacy, Reasoning and Thinking
- Pre-service Mathematics Teachers’ Statistical Reasoning About Mean
- Secondary teachers’ statistical reasoning in comparing two groups
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Over the past decade there has been an increasingly strong call for statisticseducation to focus more on statistical literacy, reasoning, and thinking. One of themain arguments presented is that traditional approaches to teaching statistics focuson skills, procedures, and computations, which do not lead students to reason orthink statistically.
This book explores the challenge posed to educators at alllevelshow to develop the desired learning goals for students by focusing oncurrent research studies that examine the nature and development of statisticalliteracy, reasoning, and thinking. We begin this introductory chapter with anoverview of the reform movement in statistics education that has led to the focus onthese learning outcomes. Next, we offer some preliminary definitions anddistinctions for these often poorly defined and overlapping terms.
We then describesome of the unique issues addressed by each chapter and conclude with somesummary comments and implications. Quantitative information is everywhere, and statistics are increasingly presentedas a way to add credibility to advertisements, arguments, or advice. Being able toproperly evaluate evidence data and claims based on data is an important skill thatall students should learn as part of their educational programs. The study of statisticsprovides tools that informed citizens need in order to react intelligently toquantitative information in the world around them.
Yet many research studiesindicate that adults in mainstream society cannot think statistically about importantissues that affect their lives. Printed in the Netherlands. As former president of the American Statistical Association, David Moore wrote, Statistics has some claim to being a fundamental method of inquiry,a general way of thinking that is more important than any of the specific techniquesthat make up the discipline p. It is not surprising, given the importance ofstatistics, that there has been an increase in the amount of statistical content includedin the elementary and secondary mathematics curriculum NCTM, and anever-increasing number of introductory statistics courses taught at the college level.
Despite the increasing need for statistics instruction, historically statisticseducation has been viewed by many students as difficult and unpleasant to learn,and by many instructors as frustrating and unrewarding to teach. As more and morestudents enroll in introductory statistics courses, instructors are faced with manychallenges in helping these students succeed in the course and learn statistics.
Someof these challenges include. It is difficult to motivate students to engage in the hardwork of learning statistics. Many students have difficulty with the underlying mathematics such asfractions, decimals, algebraic formulas , and that interferes with learning therelated statistical content. The context in many statistical problems may mislead the students, causingthem to rely on their experiences and often faulty intuitions to produce ananswer, rather than select an appropriate statistical procedure.
Students equate statistics with mathematics and expect the focus to be onnumbers, computations, formulas, and one right answer. They areuncomfortable with the messiness of data, the different possibleinterpretations based on different assumptions, and the extensive use ofwriting and communication skills.
Amidst the challenges of dealing with students poor mathematics skills, lowmotivation to learn a difficult subject, expectations about what the course should be,and reliance on faulty intuitions and misconceptions, many instructors strive toenable students to develop statistical literacy, reasoning, and thinking. There appearsto be a consensus that these are the most important goals for students enrolled instatistics classes, and that these goals are not currently being achieved.
Thedissatisfaction with students ability to think and reason statistically, even afterformally studying statistics at the college and graduate level, has led to areexamination of the field of statistics.
Todays leading statisticians see statistics as a distinct discipline, and one that isseparate from mathematics see Chapter 4. Some suggest that statistics should infact be considered one of the liberal arts e. The liberal arts imageemphasizes that statistics involves distinctive and powerful ways of thinking:Statistics is a general intellectual method that applies wherever data, variation, andchance appear. It is a fundamental method because data, variation, and chance areomnipresent in modern life.
It is an independent discipline with its own core ideasrather than, for example, a branch of mathematics Moore, , p. As the discipline has evolved and become more distinct, changes have beencalled for in the teaching of statistics. Dissatisfaction with the introductory collegecourse has led to a reform movement that includes focusing statistics instructionmore on data and less on theory Cobb, Moore describes the reform interms of changes in content more data analysis, less probability , pedagogy fewerlectures, more active learning , and technology for data analysis and simulations.
At the elementary and secondary level, there is an effort to help students developan understanding and familiarity with data analysis see Chapter 6 rather thanteaching them a set of separate skills and procedures. New K12 curricularprograms set ambitious goals for statistics education, including developing studentsstatistical reasoning and understanding e.
Several factors have led to these current efforts to change the teaching ofstatistics at all educational levels. These factors include. Changes and increases in the use of technology in the practice of statistics,and its growing availability in schools and at home.
Increased awareness of students inability to think or reason statistically,despite good performance in statistics courses. Concerns about the preparation of teachers of statistics at the K12 andcollege level, many of whom have never studied applied statistics norengaged in data analysis activities.
Many recommendations have been given for how statistics courses should betaught, as part of the general reform movement. Some of these recommendations areas follows:. Incorporate more data and concepts. Rely heavily on real not merely realistic data. Focus on developing statistical literacy, reasoning, and thinking.
Foster active learning, through various alternatives to lecturing. Encourage a broader range of attitudes, including appreciation of the power. However, despite reform efforts, many statistics courses stillteach the same progression of content and emphasize the development of skills andprocedures. Although students and instructors appear to be happier with reformedcourses, many students still leave the course perceiving statistics as a set of tools andtechniques that are soon forgotten. Pfannkuch and Wild Chapter 2 discuss howcurrent methods of teaching have often focused on the development of skills andhave failed to instill the ability to think statistically.
It is apparent, when reading articles about recommendations to reform theteaching of statistics, that there are no consistent definitions for the often statedlearning goals of literacy, reasoning, and thinking. Statistical literacy is usedinterchangeably with quantitative literacy, while statistical thinking and reasoningare used to define the same capabilities. This confusion of terms was especiallyevident at the Fifth International Conference on Teaching Statistics, held inSingapore in It became apparent that when statistics educators or researcherstalk about or assess statistical reasoning, thinking, or literacy, they may all be usingdifferent definitions and understandings of these cognitive processes.
The similarities and differences among these processes are important to considerwhen formulating learning goals for students, designing instructional activities, andevaluating learning by using appropriate assessment in. Log in Get Started. See Full Reader. Download for free Report this document. Embed Size px x x x x Ben-Zvi and J. Garfield eds. These factors include Changes in the field of statistics, including new techniques of dataexploration Changes and increases in the use of technology in the practice of statistics,and its growing availability in schools and at home Increased awareness of students inability to think or reason statistically,despite good performance in statistics courses Concerns about the preparation of teachers of statistics at the K12 andcollege level, many of whom have never studied applied statistics norengaged in data analysis activities.
Some of these recommendations areas follows: Incorporate more data and concepts. Encourage a broader range of attitudes, including appreciation of the power of statistical processes, chance, randomness, and investigative rigor, and apropensity to become a critical evaluator of statistical claims. Use alternative assessment methods to better understand and documentstudent learning.
The Challenge of Developing Statistical Literacy, Reasoning and Thinking
Authors: Theodosia Prodromou. The article investigates how to year-olds build informal conceptions of inferential statistics as they engage in a modelling process and build their own computer simulations with dynamic statistical software. This study proposes four primary phases of informal inferential reasoning for the students in the statistical modeling and simulation process. Findings show shifts in the conceptual structures across the four phases and point to the potential of all of these phases for fostering the development of students- robust knowledge of the logic of inference when using computer based simulations to model and investigate statistical questions. Commenced in January
Twenty-six pre-service teachers were tested using an open-ended problem where they were expected to analyze a method in finding the mean of a data. Three of their test results are selected to be analyzed. The results suggest that the pre-service teachers did not use context to develop the interpretation of mean. Therefore, this article also offers strategies to promote statistical reasoning about mean that use various contexts. Download this PDF file. APA: Kristanto, Y. Erwanda, Ed.
I am confident that The Challenge of Developing Statistical Literacy, Reasoning, and Thinking will be seen as a classic. PrefaceOver the past decade there has.
Pre-service Mathematics Teachers’ Statistical Reasoning About Mean
It seems that you're in Germany. We have a dedicated site for Germany. Research in statistics education is an emerging field, with much of the work being published in diverse journals across many disciplines. Locating and synthesizing this research is often a challenging task, as is connecting the research literature to practical issues of teaching and assessing students. This book is unique in that it collects, presents, and synthesizes cutting edge research on different aspects of statistical reasoning and applies this research to the teaching of statistics to students at all educational levels.
Over the past decade there has been an increasingly strong call for statisticseducation to focus more on statistical literacy, reasoning, and thinking. One of themain arguments presented is that traditional approaches to teaching statistics focuson skills, procedures, and computations, which do not lead students to reason orthink statistically. This book explores the challenge posed to educators at alllevelshow to develop the desired learning goals for students by focusing oncurrent research studies that examine the nature and development of statisticalliteracy, reasoning, and thinking. We begin this introductory chapter with anoverview of the reform movement in statistics education that has led to the focus onthese learning outcomes.
Secondary teachers’ statistical reasoning in comparing two groups
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated. The objective is to collect and collate metadata and provide full text index from several national and international digital libraries, as well as other relevant sources. It is a academic digital repository containing textbooks, articles, audio books, lectures, simulations, fiction and all other kinds of learning media. Show full item record. Dani Ben-Zvi Joan Garfield. The seventeen chapters in this volume by no means exhaust all issues related to the development of statistical literacy, reasoning, and thinking.
Statistics education is the practice of teaching and learning of statistics , along with the associated scholarly research. Statistics is both a formal science and a practical theory of scientific inquiry , and both aspects are considered in statistics education. Education in statistics has similar concerns as does education in other mathematical sciences , like logic , mathematics , and computer science. At the same time, statistics is concerned with evidence-based reasoning, particularly with the analysis of data. Therefore, education in statistics has strong similarities to education in empirical disciplines like psychology and chemistry , in which education is closely tied to "hands-on" experimentation. Mathematicians and statisticians often work in a department of mathematical sciences particularly at colleges and small universities.
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Accompanied with ongoing calls for reform in statistics curriculum, mathematics and statistics teachers purposefully have been reconsidering the curriculum and the content taught in statistics classes. Changes made are centered around statistical inference since teachers recognize that students struggle with understanding the ideas and concepts used in statistical reasoning. Despite the efforts to change the curriculum, studies are sparse on the topic of characterizing student learning and understanding of statistical inference. Moreover, there are no tools to evaluate students' statistical reasoning in a coherent way. In response to the need for a research instrument, in a series of research study, the researcher developed a reliable and valid measure to assess students' inferential reasoning in statistics IRS.
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