DOCSLIB.ORG

Guidelines for Assessment and Instruction in Statistics Education (GAISE) in Statistics Education (GAISE) College Report College

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Guidelines for Assessment and Instruction in Statistics Education (GAISE) in Statistics Education (GAISE) College Report College Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016 Committee: Robert Carver (Stonehill College), Michelle Everson, co-chair (The Ohio State University), John Gabrosek (Grand Valley State University), Nicholas Horton (Amherst College), Robin Lock (St. Lawrence University), Megan Mocko, co-chair (University of Florida), Allan Rossman (Cal Poly – San Luis Obispo), Ginger Holmes Rowell (Middle Tennessee State University), Paul Velleman (Cornell University), Jeffrey Witmer (Oberlin College), and Beverly Wood (Embry-Riddle Aeronautical University) Citation: GAISE College Report ASA Revision Committee, “Guidelines for Assessment and Instruction in Statistics Education College Report 2016,” http://www.amstat.org/education/gaise. Endorsed by the American Statistical Association July 2016 1 Contents Committee: .......................................................................................................................................................... 1 Executive Summary ............................................................................................................................................ 3 Introduction ......................................................................................................................................................... 4 Goals for Students in Introductory Statistics Courses .................................................................................... 8 Recommendations ............................................................................................................................................. 12 Suggestions for Topics that Might be Omitted from Introductory Statistics Courses ............................... 23 References .......................................................................................................................................................... 25 APPENDIX A: Evolution of Introductory Statistics and Emergence of Statistics Education Resources . 28 APPENDIX B: Multivariable Thinking .......................................................................................................... 34 APPENDIX C: Activities, Projects, and Datasets .......................................................................................... 43 APPENDIX D: Examples of Using Technology ............................................................................................. 66 APPENDIX E: Examples of Assessment Items ............................................................................................ 104 APPENDIX F: Learning Environments ....................................................................................................... 131 2 EEExecutiveExecutive Summary In 2005 the American Statistical Association (ASA) endorsed the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report. This report has had a profound impact on the teaching of introductory statistics in two- and four-year institutions, and the six recommendations put forward in the report have stood the test of time. Much has happened within the statistics education community and beyond in the intervening 10 years, making it critical to re-evaluate and update this important report. For readers who are unfamiliar with the original GAISE College Report or who are new to the statistics education community, the full version of the 2005 report can be found at http://www.amstat.org/education/gaise/GaiseCollege_full.pdf and a brief history of statistics education can be found in Appendix A of this new report. The revised GAISE College Report takes into account the many changes in the world of statistics education and statistical practice since 2005 and suggests a direction for the future of introductory statistics courses. Our work has been informed by outreach to the statistics education community and by reference to the statistics education literature. We continue to endorse the six recommendations outlined in the original GAISE College Report. We have simplified the language within some of these recommendations and re-ordered other recommendations so as to focus first on what to teach in introductory courses and then on how to teach those courses. We have also added two new emphases to the first recommendation. The revised recommendations are: 1. Teach statistical thinking. Teach statistics as an investigative process of problem-solving and decision- making. Give students experience with multivariable thinking. 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyze data. 6. Use assessments to improve and evaluate student learning. This report includes an updated list of learning objectives for students in introductory courses, along with suggested topics that might be omitted from or de-emphasized in an introductory course. In response to feedback from statistics educators, we have substantially expanded and updated some appendices. We also created some new appendices to provide details about the evolution of introductory statistics courses; examples involving multivariable thinking; and ideas for implementing the GAISE recommendations in a variety of different learning environments. 3 Introduction Background Much has changed since the ASA endorsed the Guidelines for Assessment and Instruction in Statistics Education College Report (hereafter called the GAISE College Report) in 2005. Some highlights include: • More students are studying statistics. According to the Conference Board on Mathematical Sciences (CBMS) survey, 508,000 students took an introductory statistics course in a two- or four-year college/university in the fall of 2010, a 34.7% increase from 2005. More than a quarter (27.0%) of these enrollments were at two-year colleges 1. Nearly 200,000 students took the Advanced Placement (AP) Statistics exam in 2015, an increase of more than 150% over 2005. In addition, many high school students took the AP course without taking the exam or took a non-AP statistics course. At the undergraduate level, the number of students completing an undergraduate major in Statistics grew by more than 140% between 2003 and 2013 and continues to grow rapidly 2. • Many students are exposed to statistical thinking in grades 6 – 12 , because more state standards include a considerable number of statistical concepts and methods. Many of these standards have been influenced by the GAISE PreK – 12 report developed and endorsed by the ASA 3. In particular, the Common Core 4 includes standards on interpreting categorical and quantitative data and on making inferences and justifying conclusions. • The rapid increase in available data has made the field of statistics more salient. Many have heralded the flood of information now available. The Economist published a special report on the “data deluge” in 2010 5. Statisticians such as Hans Rosling and Nate Silver have achieved celebrity status by demonstrating how to garner insights from data 6. • The discipline of Data Science has emerged as a field that encompasses elements of statistics, computer science, and domain-specific knowledge 7. Data science has been described as the interplay between computational and inferential thinking 8. It includes the analysis of data types such as text, audio, and video, which are becoming more 1 http://www.ams.org/profession/data/cbms-survey/cbms2010-Report.pdf 2 http://www.amstat.org/education/curriculumguidelines.cfm 3 http://www.amstat.org/education/gaise/GAISEPreK-12_Full.pdf 4 http://www.corestandards.org/ 5 http://www.economist.com/printedition/2010-02-27 6 Silver, N. (2012) The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t. New York: Penguin Books. Also see http://www.fivethirtyeight.com and http://www.gapminder.org. 7 http://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/ 8 Jordan, M. (2016) Computational Thinking and Inferential Thinking: Foundations of Data Science eCOTS 2016. Also see at https://www.causeweb.org/cause/ecots/ecots16/keynotes/jordan. 4 prevalent. There has been a parallel development of “analytics” as the study of extracting information from big data—particularly with business and governmental applications. • More and better technology options for education have become widely available . These include course management systems, automated homework systems, technology for facilitating discussion and engagement, audience response systems, and videos now used in many courses. Applets and other applications, such as Shiny apps coded in the R programming language, that are designed to explore statistical concepts have come into widespread use. Many general-purpose statistical packages have developed functions specifically for teaching and learning. • Alternative learning environments have become more popular. These include online courses, hybrid courses, flipped classrooms, and Massively Open Online Courses (MOOCs). Many of these environments may be particularly helpful for supporting faculty development. • Some have called for an update to the consensus introductory statistics curriculum to account for the rich data that are available to answer important statistical questions. • Innovative ways to teach the logic of statistical inference have received increasing attention. Among these are greater use of computer-based simulations and the use of resampling methods (randomization tests and bootstrapping) to teach concepts
Load more

Recommended publications
  • GAISE II Pre-K–12 Report
    Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) A Framework for Statistics and Data Science Education Writing Committee Anna Bargagliotti (co-chair) Sheri Johnson Loyola Marymount University University of Georgia* Christine Franklin (co-chair) Leticia Perez American Statistical Association University of California Los Angeles Center X Pip Arnold Denise A. Spangler Karekare Education New Zealand University of Georgia Rob Gould University of California Los Angeles The Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) is an official position of the National Council of Teachers of Mathematics as approved by the NCTM Board of Directors, February 2020. Endorsed by the American Statistical Association, November 2020. *Current affiliation -The Mount Vernon School Library of Congress Cataloging-in-Publication Data Names: Bargagliotti, Anna, author. Title: Pre-K–12 guidelines for assessment and instruction in statistics education II (GAISE II) / writing committee, Anna Bargagliotti (co-chair), Loyola Marymount University, Christine Franklin (co-chair), American Statistical Association, Pip Arnold, Karekare Education New Zealand, Rob Gould, University of California Los Angeles, Sheri Johnson, University of Georgia, Leticia Perez, University of California Los Angeles, Denise A. Spangler, University of Georgia. Other titles: Guidelines for assessment and instruction in statistics education (GAISE) report Description: Second edition. | Alexandria, VA : American Statistical Association, 2020. | Includes bibliographical references. | Summary: “This document lays out a curriculum framework for Pre-K–12 educational programs that is designed to help students achieve data literacy and become statistically literate. The framework and subsequent sections in this book recommend curriculum and implementation strategies covering Pre-K–12 statistics education”-- Provided by publisher.
    [Show full text]
  • The Condition of Education 2005 (NCES 2005–094)
    U.S. Department of Education Institute of Education Sciences NCES 2005–094 The Condition of Education 2005 U.S. Department of Education Institute of Education Sciences NCES 2005–094 June 2005 John Wirt Project Director National Center for Education Statistics Susan Choy MPR Associates, Inc. Patrick Rooney William Hussar National Center for Education Statistics Stephen Provasnik Gillian Hampden-Thompson Education Statistics Services Institute Production: Barbara Kridl Managing Editor Andrea Livingston Senior Editor MPR Associates, Inc. U.S. Department of Education Margaret Spellings Secretary Institute of Education Sciences Grover J. Whitehurst Director National Center for Education Statistics Grover J. Whitehurst Acting Commissioner The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations. It fulfi lls a congressional mandate to collect, collate, analyze, and report full and complete statistics on the condition of education in the United States; conduct and publish reports and specialized analyses of the meaning and signifi cance of such statistics; assist state and local education agencies in improving their statistical systems; and review and report on education activities in foreign countries. NCES activities are designed to address high priority education data needs; provide consistent, reliable, complete, and accurate indicators of education status and trends; and report timely, useful, and high quality data to the U.S. Department of Education, the Congress, the states, other education policymakers, practitioners, data users, and the general public. We strive to make our products available in a variety of formats and in language that is appropriate to a variety of audiences.
    [Show full text]
  • Statistics Education and the Role of Technology
    Statistics Education and the Role of Technology Gail Burrill Mathematical Sciences Education Board, National Research Council, Washington, D.C. The world is changing dramatically. CD’s, the web, caller ID, grocery store debit machines, MRI’s: life today is very different from life thirty years ago. Computer assisted design, spreadsheet cost analysis, simulations, instant communication across cities and nations: the business world is very different from the business world thirty years ago. Information technology is transforming the way we do business and the way we live, and all indications are that even more change is imminent. In education just as in society and in the workforce, technology has the potential to make a profound impact. It can change the nature of the content we teach and the way we teach that content. In statistics, that potential is well on its way to becoming a reality. Although a Danish astronomer, Tyco Brahe, (1546-1601) was one of the first scientists to confront the problem of measurement error (Freedman et al, 1996), even in the early 1700's the nature of statistics was primarily qualitative probably due to the fact that there was very little quantitative data available (Ottaviani, 1989). Quantitative information became a reality in Hungary in 1784 with the first census; the first U.S. census was in 1790. This data was very difficult to manage, and by the late 1880's, a crisis had developed in the attempt to organize and process the large amounts of data collected through the U.S. census. While mathematicians and statisticians had continued over the years to develop statistics as a field, Herman Hollerith in response to this crisis produced a machine that would handle tabulations mechanically (Wallechinsky & Wallace, 1975).
    [Show full text]
  • Research Notes Research Notes
    Research Notes Office of Research and PsychometricsDevelopment RN-23,RN-38, November February 20052009 Advanced Placement® Statistics Students’ Education Choices After High School Brian F. Patterson Introduction were more likely than non-AP examinees to take courses in the discipline of the exam. While Morgan and Maneckshana The College Board’s Advanced Placement® (AP®) Program did not analyze the relationship of AP Statistics participation was founded in 1955 as a national testing program with the and subsequent course taking, they did report a very robust goal of providing high school students opportunities to take finding across 25 exams and 21 colleges and universities. college-level courses. As of the 2009 administration, the AP One study that analyzed the more general relation- Program will consist of 37 courses in 20 different subject ship of high school mathematics and science course work areas. Despite the tremendous growth of the program, the with choice of a math or science major in college was College Board has not been able to systematically track what Trusty (2002). Trusty estimated separate models for men happens to the exam-taking population after they are exam- and women, given previous findings of significant gender ined, nor has it any information on students who take an AP interaction effects. He found that even after controlling course, but not the corresponding AP Exam. for racial/ethnic group, socioeconomic status, eighth-grade There is the expectation that students participating mathematics and science exam scores, and high school edu- in the Advanced Placement Program tend to be more likely cational attitudes and behaviors, course-taking patterns in than nonparticipants to choose postsecondary course work high school do have a significant relationship with choice of that is related to the discipline in which they are examined, a math or science major.
    [Show full text]
  • A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol
    A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol. 43, No. 3, pp. 253–278 | ©2011 ISTE | iste.org A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research Karen Larwin Youngstown State University David Larwin Kent State University at Salem Abstract The present meta-analysis is a comprehensive investigation of the effectiveness of computer-assisted instruction (CAI) on student achievement in postsec- ondary statistics education across a forty year period of time. The researchers calculated an overall effect size of 0.566 from 70 studies, for a total of 219 effect-size measures from a sample of n = 40,125 participants. These results suggest that the typical student moved from the 50th percentile to the 73rd percentile when technology was used as part of the curriculum. This study demonstrates that subcategories can further the understanding of how the use of CAI in statistics education might be maximized. The study discusses im- plications and limitations. (Keywords: statistics education, computer-assisted instruction, meta-analysis) iscovering how students learn most effectively is one of the major goals of research in education. During the last 30 years, many re- Dsearchers and educators have called for reform in the area of statistics education in an effort to more successfully reach the growing population of students, across an expansive variety of disciplines, who are required to complete coursework in statistics (e.g., Cobb, 1993, 2007; Garfield, 1993, 1995, 2002; Giraud, 1997; Hogg, 1991; Lindsay, Kettering, & Siegmund, 2004; Moore, 1997; Roiter, & Petocz, 1996; Snee, 1993;Yilmaz, 1996).
    [Show full text]
  • FDPS PRICE DOCUMENT RESUME AC 006 502 Continuing
    DOCUMENT RESUME ED 036 722 AC 006 502 AUTHOR Royce, Marion TITLE Continuing Education for Women in Canada; Trends and Opportunities. Monographs in Adult Education, 4. INSTITUTION Ontario Inst. for Studies in Education, Toronto. Dept. of Adult Education. PUB DATF Sep 69 NCTE 174p. FDPS PRICE EDRS Price MF-$0.75 HC-$8.80 DESCRIPTORS Correspondence Study, Discussion Groups, Educational Television, English (Second Language), Immigrants, Manpower Development, Nursing, *Professional Continuing Education, Public Affairs Education, *Public School Adult Education, Teacher Education, *University Extension, Volunteers, *Womens Education IDENTIFIERS *Canada ABSTRACT This report describes a number of innovative continuing education programs for Canadianwomen under the auspices of universities, local educational authorities, and other organizations. It covers daytime (largely part time)classes at Mount St. Vincent University, the Thomas More Institute, and the Universities of British Columbia, Calgary, Guelph,and Manitoba; offerings by extension departments and continuingeducation centers at McGill, the University of Toronto, and the Universitiesof Alberta, British Columbia, Calgary, Guelphand Manitoba; and activities of the Adult Education Division of theCalgary School Board, including those in cooperation with the Universityof Calgary. It also deals with a public affairs educationprogram in Toronto, training of volunteers by and for the National Councilof Jewish women, discussion groups sponsored by the Young Women's Christian Association, career
    [Show full text]
  • Statement of Teaching Philosophy Karen E. Nielsen My Primary Goal As an Instructor Is to Help My Students Develop A
    Statement of Teaching Philosophy Karen E. Nielsen My primary goal as an instructor is to help my students develop a strong statistical intuition and the ability to confidently and concisely communicate about their thought process. These are skills that make an excellent statistician, but also an excellent scientist or informed citizen. I think that intuition makes the difference between frustration and appreciation in statistics. In the courses I struggled with in graduate school, I found that I did not understand why things worked the way they did and was frequently surprised by the results I got. Once I was able to develop an intuition that helped me bridge the gap between the theory and applications, I was able to enjoy working through problems and I got excited when results went against my expectations, because that meant I may have found something meaningful, or at least discovered an opportunity to learn and modify my intuition to help me in the future. For example, I want an introductory student to be able to read a problem with example values and know that they expect a fairly small p­value. If they end up getting a large p­value, I want them stop and check their work. They should make sure they used tables, their calculator, and formulas correctly. It’s possible that their intuition was incorrect, so reviewing these concrete steps will help them feel more confident in their answer. Once students have begun to build their intuition, I like to introduce problems that are surprising and may go against expectations that students may have.
    [Show full text]
  • Forum Guide to Taking Action with Education Data. (NFES 2013-801)
    National Cooperative Education Statistics System The National Center for Education Statistics (NCES) established the National Cooperative Education Statistics System (Cooperative System) to assist in producing and maintaining comparable and uniform information and data on early childhood, elementary, and secondary education. These data are intended to be useful for policymaking at the federal, state, and local levels. The National Forum on Education Statistics (the Forum) is an entity of the Cooperative System and, among its other activities, proposes principles of good practice to assist state and local education agencies in meeting this purpose. The Cooperative System and the Forum are supported in these endeavors by resources from NCES. Publications of the Forum do not undergo the same formal review required for products of NCES. The information and opinions published here are those of the Forum and do not necessarily represent the policy or views of the U.S. Department of Education or NCES. December 2012 This publication and other publications of the National Forum on Education Statistics may be found at the websites listed below. The NCES Home Page address is http://nces.ed.gov The NCES Publications and Products address is http://nces.ed.gov/pubsearch The Forum Home Page address is http://nces.ed.gov/forum This publication was prepared in part under Contract No. ED-CFO-10-A-0126/0002 with Quality Information Partners, Inc. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Suggested Citation National Forum on Education Statistics. (2012). Forum Guide to Taking Action with Education Data.
    [Show full text]
  • TOP 10 BENEFITS of STATISTICS in SCHOOLS for TEACHERS the Statistics in Schools (SIS) Program of the U.S
    TOP 10 BENEFITS OF STATISTICS IN SCHOOLS FOR TEACHERS The Statistics in Schools (SIS) program of the U.S. Census Bureau provides data, tools, and activities that educators can incorporate into their lessons to help teach statistics concepts and data analysis skills to students. The activities and resources are segmented by subject (English, geography, history and social studies, mathematics and statistics, and sociology) and grade (from kindergarten through high school) so statistics education can be brought to any classroom. Here are the top 10 reasons the SIS program is good for the classroom: 1 The online resources are free for K-12 teachers. Educators can access, at no charge, more than 100 downloadable activities and resources on the website: www.census.gov/schools. The activities on the website are searchable by grade, school subject, topic, and education standard. 2 Experts from the U.S. Census Bureau contributed to the program. The Census Bureau, which launched SIS, is the primary source of economic and demographic data for the United States. Census Bureau statistical experts were involved in the creation of the program activities, making SIS uniquely qualified to support statistics education. 3 The program promotes cross-curricular education. SIS uses a broad array of Census Bureau data to provide activities and resources for courses in English, geography, history and social studies, mathematics and statistics, and sociology. 4 SIS offers a number of resources and tools, including: • Activities • Games • Maps • Infographics and data visualizations • News articles • Data tools that reveal population statistics • Videos by sex, age, ethnicity, and race census.gov/schools 5 SIS activities were developed by teachers for teachers.
    [Show full text]
  • Teaching Statistics with a Critical Pedagogy
    TEACHING STATISTICS WITH A CRITICAL PEDAGOGY A Dissertation by TONI DIMELLA Submitted to the School of Graduate Studies at Appalachian State University in partial fulfillment of the requirements for the degree of DOCTOR OF EDUCATION August 2019 Educational Leadership Doctoral Program Reich College of Education TEACHING STATISTICS WITH A CRITICAL PEDAGOGY A Dissertation by TONI DIMELLA August 2019 APPROVED BY: Tracy Goodson-Epsy, Ed.D Chairperson, Dissertation Committee Lisa Poling, Ph.D Member, Dissertation Committee Tracie Salinas, Ph.D Member, Dissertation Committee Vachel Miller, Ed.D Director, Educational Leadership Doctoral Program Mike McKenzie, Ph.D. Dean, Cratis D. Williams School of Graduate Studies Copyright by Toni DiMella 2019 All Rights Reserved Abstract TEACHING STATISTICS WITH A CRITICAL PEDAGOGY Toni DiMella A.A.S., SUNY Sullivan B.A., Mount Saint Mary College M.S.Ed., Mount Saint Mary College Ed.S., Appalachian State University Ed.D., Appalachian State University Dissertation Committee Chairperson: Dr. Tracy Goodson-Epsy After President Obama shifted the country’s focus from K-12 towards higher education, post-secondary schools found themselves under significant public, financial, and political pressure. To close the achievement gap and meet new standards of accountability, higher education institutions began looking for methods to increase student access and success. With a growing emphasis on degree completion and quantitative literacy, some researchers began to explore the use of critical pedagogies to better serve a more diverse population (Ukpokodu, 2011). This quantitative study measured the effectiveness of implementing a critical statistics pedagogy in an undergraduate introductory statistics classroom and its impact on course success, persistence, and mathematical empowerment.
    [Show full text]
  • The Gap Between Statistics Education and Statistical Practice
    The Gap Between Statistics Education and Statistical Practice Robert E. KASS As I write this response to George Cobb’s call to rebuild the cians from other mathematically and computationally sophisti- statistics curriculum, I am returning from a symposium, “Statis- cated data analysts? Our conclusion was that, roughly speaking, tics in the 21st Century,” aimed at helping to define goals of “statistical thinking uses probabilistic descriptions of variabil- a new center for statistics at MIT (which has been an outlier ity in (1) inductive reasoning and (2) analysis of procedures for among premier U.S. universities in not having a statistics de- data collection, prediction, and scientific inference.” It was not partment). To me, the most striking aspect of the symposium our intention to confine statistical education to those topics that was the consistency among its speakers in their admiration for involve statistical models, and I again agree with Cobb (as we the discipline of statistics, which focuses on the foundation of argued also in B&K) that there is too much emphasis on the science and engineering: the use of data to provide information subtleties of mathematics-based statistical logic in many statis- about the world. Maintaining this foundation as technology ad- tics courses. However, I would not back off the B&K formula- vances is a noble endeavor and, in the past few years, partly due tion of what differentiates statistical approaches to data analy- to the advent of Data Science and Big Data, the importance of sis, and I continue to advocate it as an overarching guide when statistics has become much more widely appreciated.
    [Show full text]
  • The State of the Right to Education Worldwide Free Or Fee: 2006
    The State of the Right to Education Worldwide Free or Fee: 2006 Global Report Katarina Tomasevski Copenhagen, August 2006 Katarina Tomasevski - Free or Fee: 2006 Global Report i Table of Contents Preface, acknowledgments and anti-acknowledgments.........................................................x INTRODUCTION Why don’t we have a single global strategy for education? ...............................................xiv How and why the existing global targets nullify human rights? ........................................xix Why education should not have been entrusted to World Bank’s economists? .................xxi How would human rights law alter global targets? ..........................................................xxiv Why are human rights violators bad educators?...............................................................xxvi Why should we care?........................................................................................................xxix SUB-SAHARAN AFRICA Educational toll of World Bank’s recipes..............................................................................1 Freeing education from debt bondage....................................................................................4 Constitutional guarantees versus fiscal policies ....................................................................6 Educational cost of military expenditure ...............................................................................9 Troubling global targetology ...............................................................................................12
    [Show full text]