Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1677199
B. Stone, D. Kay, A. Reynolds
Abstract Instructors of postsecondary classes in statistics rely heavily on visuals in their teaching, both within the classroom and in resources like textbooks, handouts, and software, but this information is often inaccessible to students who are blind or visually impaired (BVI). The unique challenges involved in adapting both pedagogy and course materials to accommodate a BVI student may provoke anxiety among instructors teaching a BVI student for the first time, and instructors may end up feeling unprepared or “reinventing the wheel.” We discuss a wide variety of accommodations inside and outside of the classroom grounded in the empirical literature on cognition and learning and informed by our own experience teaching a blind student in an introductory statistics course.
{"title":"Teaching Visually Impaired College Students in Introductory Statistics","authors":"B. Stone, D. Kay, A. Reynolds","doi":"10.1080/10691898.2019.1677199","DOIUrl":"https://doi.org/10.1080/10691898.2019.1677199","url":null,"abstract":"Abstract Instructors of postsecondary classes in statistics rely heavily on visuals in their teaching, both within the classroom and in resources like textbooks, handouts, and software, but this information is often inaccessible to students who are blind or visually impaired (BVI). The unique challenges involved in adapting both pedagogy and course materials to accommodate a BVI student may provoke anxiety among instructors teaching a BVI student for the first time, and instructors may end up feeling unprepared or “reinventing the wheel.” We discuss a wide variety of accommodations inside and outside of the classroom grounded in the empirical literature on cognition and learning and informed by our own experience teaching a blind student in an introductory statistics course.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"225 - 237"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1677199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45611344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1687369
Aimee Schwab-McCoy
Abstract The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in “client departments” such as business, the social sciences, and the lab sciences, typically taught by non-statisticians. This article presents the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (1) what is currently being taught and how, and (2) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines may be a welcome step toward improving statistics instruction across the university.
{"title":"The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University","authors":"Aimee Schwab-McCoy","doi":"10.1080/10691898.2019.1687369","DOIUrl":"https://doi.org/10.1080/10691898.2019.1687369","url":null,"abstract":"Abstract The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in “client departments” such as business, the social sciences, and the lab sciences, typically taught by non-statisticians. This article presents the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (1) what is currently being taught and how, and (2) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines may be a welcome step toward improving statistics instruction across the university.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"253 - 264"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1687369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46898799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1647768
Jennifer Broatch, S. Dietrich, Don Goelman
Abstract Early exposure to data science skills, such as relational databases, is essential for students in statistics as well as many other disciplines in an increasingly data driven society. The goal of the presented pedagogy is to introduce undergraduate students to fundamental database concepts and to illuminate the connection between these database concepts and the functionality provided by the dplyr package for R. Specifically, students are introduced to relational database concepts using visualizations that are specifically designed for students with no data science or computing background. These educational tools, which are freely available on the Web, engage students in the learning process through a dynamic presentation that gently introduces relational databases and how to ask questions of data stored in a relational database. The visualizations are specifically designed for self-study by students, including a formative self-assessment feature. Students are then assigned a corresponding statistics lesson to utilize statistical software in R within the dplyr framework and to emphasize the need for these database skills. This article describes a pilot experience of introducing this pedagogy into a calculus-based introductory statistics course for mathematics and statistics majors, and provides a brief evaluation of the student perspective of the experience. Supplementary materials for this article are available online.
{"title":"Introducing Data Science Techniques by Connecting Database Concepts and dplyr","authors":"Jennifer Broatch, S. Dietrich, Don Goelman","doi":"10.1080/10691898.2019.1647768","DOIUrl":"https://doi.org/10.1080/10691898.2019.1647768","url":null,"abstract":"Abstract Early exposure to data science skills, such as relational databases, is essential for students in statistics as well as many other disciplines in an increasingly data driven society. The goal of the presented pedagogy is to introduce undergraduate students to fundamental database concepts and to illuminate the connection between these database concepts and the functionality provided by the dplyr package for R. Specifically, students are introduced to relational database concepts using visualizations that are specifically designed for students with no data science or computing background. These educational tools, which are freely available on the Web, engage students in the learning process through a dynamic presentation that gently introduces relational databases and how to ask questions of data stored in a relational database. The visualizations are specifically designed for self-study by students, including a formative self-assessment feature. Students are then assigned a corresponding statistics lesson to utilize statistical software in R within the dplyr framework and to emphasize the need for these database skills. This article describes a pilot experience of introducing this pedagogy into a calculus-based introductory statistics course for mathematics and statistics majors, and provides a brief evaluation of the student perspective of the experience. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"147 - 153"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1647768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45623089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1669507
Robert W. Hayden
Abstract Recent years have seen increasing interest in incorporating resampling methods into introductory statistics courses and the high school mathematics curriculum. While the use of permutation tests for data from experiments is a step forward, the use of simple bootstrap methods for sampling situations is more problematical. This article demonstrates via counterexamples that many of the claims made for these simple methods are simply wrong. Their use with beginners can only be justified after their true properties have been fully researched, and their many limitations explained to students. Supplementary materials for this article are available online.
{"title":"Questionable Claims for Simple Versions of the Bootstrap","authors":"Robert W. Hayden","doi":"10.1080/10691898.2019.1669507","DOIUrl":"https://doi.org/10.1080/10691898.2019.1669507","url":null,"abstract":"Abstract Recent years have seen increasing interest in incorporating resampling methods into introductory statistics courses and the high school mathematics curriculum. While the use of permutation tests for data from experiments is a step forward, the use of simple bootstrap methods for sampling situations is more problematical. This article demonstrates via counterexamples that many of the claims made for these simple methods are simply wrong. Their use with beginners can only be justified after their true properties have been fully researched, and their many limitations explained to students. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"208 - 215"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1669507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41916768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1693194
Sarah Abramowitz, Drew U. Vittorio Addona, Macalester College Daniel Adrian, Grand Valley State U. James Albert, Bowling Green State U. K. Scott Alberts, Truman State U. Ming-Wen An, Vassar College Kirk Anderson, Grand Valley State U. Elizabeth Arnold, James Madison U. Brittney Bailey, Amherst College Anna Bargagliotti, Loyola Marymount U. Ben Barnard, Wells Fargo and Co. Barb Barnet, U. of Wisconsin Platteville Sheila Barron, U. of Iowa Silas Bergen, Winona State U. Bruce Blaine, St. John Fisher College Erin Blankenship, U. of Nebraska-Lincoln Andrej Blejec, National Institute of Biology Kelly Bodwin, California Polytechnic State U. Charlotte Bolch, U. of Florida Sean Bradley, Clarke U. Thomas Braun, U. of Michigan Andrew Bray, Reed College Ann Brearley, U. of Minnesota System Jennifer Broatch, Arizona State U. Ann Cannon, Cornell College Yongtao Cao, Indiana U. of Pennsylvania College Ben Capistrant, Smith College Bruce Carlson, Ohio U. Rob Carver, Stonehill College Catherine Case, U. of Georgia Mine Cetinkaya-Rundel, Duke U. Laura Chihara, Carleton College Adam Childers, Roanoke College William Cipolli, Colgate U. Jessi Cisewski-Kehe, Yale U. Richard Cleary, Babson College Katharine Correia, Amherst College Carolyn Cuff, Westminster College Allison Davidson, Muhlenberg College Leandro de Souza, U. Federal de Uberlândia Melody Denhere, U. of Mary Washington Concetta DePaolo, Indiana State U. Mine Dogucu, Denison U. Jillian Downey, Truman State U. Jonathan Duggins, North Carolina State U. Bruce Dunham, U. of British Columbia Felicity Enders, Mayo Clinic Erik Erhardt, U. of Minnesota Robert Erhardt, Wake Forest U. Diane Evans, Rose-Hulman Institute Of Technology Camille Fairbourn, Michigan State U. Jeffrey Farmer, New Orleans Baptist Theological Seminary Pamela Fellers, Grinnell Coll Derek Feng, Yale U. Jacob Fiksel, Johns Hopkins U. Jack Follis, U. of St. Thomas Steven Foti, U. of Florida Marian Frazier, College of Wooster Peter Freeman, Carnegie Mellon U. Daniel Frischemeier, U. Paderborn Fakultat John Gabrosek, Grand Valley State U. Iddo Gal, U. of Haifa Marybeth Gasman, U. of Pennsylvania Varghese George, Augusta U. Ken Gerow, U. of Wyoming Natasha Gerstenschlager, Western Kentucky U. Santu Ghosh, Augusta U. A. Jonathan Godfrey, Inst of Fundamental Sceinces Burke Grandjean, U. of Wyoming Jennifer Green, Montana State U. Ellen Gundlach, Purdue U. Debbie Hahs-Vaughn, U. of Central Florida Josephine Hamer, Western Connecticut State U. Stacey Hancock, Montana State U. Kathryn Hanford, U. of Nebraska Lincoln Johanna Hardin, Pomona College Leigh Harrell-Williams, U. of Memphis Matt Hayat, Georgia State U. Sarai Hedges, U. of Cincinnati Jacqueline Herman, Northern Kentucky U. Dawn Holmes, U. of California Santa Barbara Yi-Chun Hong, Arizona State Univ Tisha Hooks, Winona State U. Nicholas Horton, Amherst College Leanna House, Virginia Tech Jingchen (Monika) Hu, Vassar College Patricia Humphrey, Georgia Southern U. Debra Hydorn, U. of Mary Washington
Sarah Abramowitz、Drew U.Vittorio Addona、Macalester College Daniel Adrian、Grand Valley State U.James Albert、Bowling Green State U.K.Scott Alberts、Truman State U.Ming-Wen An、Vassar College Kirk Anderson、大谷州立大学U.Elizabeth Arnold、James Madison U.Brittney Bailey、Amherst College Anna Barggliotti、Loyola Marymont U.Ben Barnard、Wells Fargo and Co.Barb Barnet、，威斯康星大学普拉特维尔分校Sheila Barron、爱荷华大学Silas Bergen、威诺纳州立大学Bruce Blaine、圣约翰费舍尔学院Erin Blankenship、内布拉斯加大学Lincoln Andrej Blejec、国家生物研究所Kelly Bodwin、加州理工州立大学Charlotte Bolch、佛罗里达大学Sean Bradley、Clarke U.Thomas Braun、密歇根大学Andrew Bray、里德学院Ann Brearley，明尼苏达大学Jennifer Broatch、亚利桑那州立大学Ann Cannon、康奈尔大学曹永涛、印第安纳宾夕法尼亚大学Ben Capistrant、史密斯学院Bruce Carlson、俄亥俄州立大学Rob Carver、Stonehill学院Catherine Case、乔治亚州立大学Mine Cetinkaya Rundel、杜克大学Laura Chihara、卡尔顿大学Adam Childers、罗阿诺克学院William Cipolli、高露洁大学Jessi Cisewski Kehe、耶鲁大学。Richard Cleary，Babson College Katharine Correia，Amherst College Carolyn Cuff，Westminster College Allison Davidson，Muhlenberg College Leandro de Souza，U.Federal de Uberlândia Melody Denhere，U.of Mary Washington Concetta DePaolo，Indiana State U.Mine Dogucu，Denison U.Jillian Downey，Truman State U.Jonathan Duggins，North Carolina State U.Bruce Dunham，不列颠哥伦比亚大学Felicity Enders、梅奥诊所Erik Erhardt、明尼苏达大学Robert Erhardt，维克森林大学Diane Evans，Rose Hulman理工学院Camille Fairbourn，密歇根州立大学Jeffrey Farmer，新奥尔良浸礼会神学院Pamela Fellers，Grinnell Coll Derek Feng，耶鲁大学Jacob Fiksel，约翰霍普金斯大学Jack Follis，圣托马斯大学Steven Foti，佛罗里达大学Marian Frazier、伍斯特学院Peter Freeman、卡内基梅隆大学Daniel Frischemeier、Paderborn Fakulat John Gabrosek、Grand Valley State大学Iddo Gal、海法大学Marybeth Gasman、宾夕法尼亚大学Varghese George、Augusta U.Ken Gerow、怀俄明州大学Natasha Gerstenschlager、肯塔基州西部大学Santu Ghosh、Augusda U.A.Jonathan Godfrey，基础科学研究所Burke Grandjean，怀俄明州大学Jennifer Green，蒙大拿州大学Ellen Gundlach，普渡大学Debbie Hahs Vaughn，中佛罗里达州大学Josephine Hamer，康涅狄格州西部大学Stacey Hancock，蒙大拿州大学Kathryn Hanford，内布拉斯加大学Lincoln Johanna Hardin，波莫纳学院Leigh Harrell Williams，孟菲斯大学Matt Hayat，佐治亚州大学Sarai Hedges，辛辛那提大学杰奎琳·赫尔曼、肯塔基州北部大学道恩·霍尔姆斯、加利福尼亚大学圣巴巴拉·易春红、亚利桑那州立大学蒂莎·胡克、威诺纳州立大学尼古拉斯·霍顿、阿默斯特学院Leanna House、弗吉尼亚理工大学胡静、瓦萨学院帕特里夏·汉弗莱、佐治亚州南部大学黛布拉·海多恩、玛丽·华盛顿大学凯鲁尔·伊斯拉姆、密歇根州东部大学托德·艾弗森，威诺纳州立大学Michael Jirutek、Campbell U.Galin Jones、明尼苏达州立大学Leigh Johnson、Capital U.Brian Jones、Kenyon College Galin Jones，明尼苏达州立大学Jeff Jonkman、Grinnell College Nicola Justice、Pacific Lutheran U.Hilary Kalagher、Drew U.Laura Kapitula、Grand Valley State U.Jennifer Kaplan、乔治亚州立大学Steve Kass、，巴布森学院Eileen King，辛辛那提儿童医院Med Ctr Allyson Kiss，明尼苏达大学Shonda Kuiper，格林内尔学院Sharon Lane Getaz，圣奥拉夫学院Laura Le，明尼苏达大学Erin Leatherman，肯扬学院Timo Leuders，弗赖堡大学John Loase，康考迪亚学院Jennifer Lovett，田纳西州中部大学Adam Loy，卡尔顿学院Yi Lu，俄亥俄州立大学Karsten Luebke，FOM U.Justin Luningham，Emory U.M.Leigh Lunsford，Longwood U.Michael Mahometa，德克萨斯大学奥斯汀分校Dalisey Maligaig，菲律宾大学Los Banos Chris Malone，威诺纳州立大学Catherine Manly，马萨诸塞大学Amherst Wilmina Marget，奥格斯堡大学Susan Martonosi，Harvey Mudd College Daniele Mastrangelo，ISAC-CNR Karsten Maurer，迈阿密大学Kelly McConville，里德学院Karen McGaughey，加州理工州立大学Monnie McGee，南卫理公会大学Herle McGowan，北卡罗来纳州
{"title":"Editorial Collaborators","authors":"","doi":"10.1080/10691898.2019.1693194","DOIUrl":"https://doi.org/10.1080/10691898.2019.1693194","url":null,"abstract":"Sarah Abramowitz, Drew U. Vittorio Addona, Macalester College Daniel Adrian, Grand Valley State U. James Albert, Bowling Green State U. K. Scott Alberts, Truman State U. Ming-Wen An, Vassar College Kirk Anderson, Grand Valley State U. Elizabeth Arnold, James Madison U. Brittney Bailey, Amherst College Anna Bargagliotti, Loyola Marymount U. Ben Barnard, Wells Fargo and Co. Barb Barnet, U. of Wisconsin Platteville Sheila Barron, U. of Iowa Silas Bergen, Winona State U. Bruce Blaine, St. John Fisher College Erin Blankenship, U. of Nebraska-Lincoln Andrej Blejec, National Institute of Biology Kelly Bodwin, California Polytechnic State U. Charlotte Bolch, U. of Florida Sean Bradley, Clarke U. Thomas Braun, U. of Michigan Andrew Bray, Reed College Ann Brearley, U. of Minnesota System Jennifer Broatch, Arizona State U. Ann Cannon, Cornell College Yongtao Cao, Indiana U. of Pennsylvania College Ben Capistrant, Smith College Bruce Carlson, Ohio U. Rob Carver, Stonehill College Catherine Case, U. of Georgia Mine Cetinkaya-Rundel, Duke U. Laura Chihara, Carleton College Adam Childers, Roanoke College William Cipolli, Colgate U. Jessi Cisewski-Kehe, Yale U. Richard Cleary, Babson College Katharine Correia, Amherst College Carolyn Cuff, Westminster College Allison Davidson, Muhlenberg College Leandro de Souza, U. Federal de Uberlândia Melody Denhere, U. of Mary Washington Concetta DePaolo, Indiana State U. Mine Dogucu, Denison U. Jillian Downey, Truman State U. Jonathan Duggins, North Carolina State U. Bruce Dunham, U. of British Columbia Felicity Enders, Mayo Clinic Erik Erhardt, U. of Minnesota Robert Erhardt, Wake Forest U. Diane Evans, Rose-Hulman Institute Of Technology Camille Fairbourn, Michigan State U. Jeffrey Farmer, New Orleans Baptist Theological Seminary Pamela Fellers, Grinnell Coll Derek Feng, Yale U. Jacob Fiksel, Johns Hopkins U. Jack Follis, U. of St. Thomas Steven Foti, U. of Florida Marian Frazier, College of Wooster Peter Freeman, Carnegie Mellon U. Daniel Frischemeier, U. Paderborn Fakultat John Gabrosek, Grand Valley State U. Iddo Gal, U. of Haifa Marybeth Gasman, U. of Pennsylvania Varghese George, Augusta U. Ken Gerow, U. of Wyoming Natasha Gerstenschlager, Western Kentucky U. Santu Ghosh, Augusta U. A. Jonathan Godfrey, Inst of Fundamental Sceinces Burke Grandjean, U. of Wyoming Jennifer Green, Montana State U. Ellen Gundlach, Purdue U. Debbie Hahs-Vaughn, U. of Central Florida Josephine Hamer, Western Connecticut State U. Stacey Hancock, Montana State U. Kathryn Hanford, U. of Nebraska Lincoln Johanna Hardin, Pomona College Leigh Harrell-Williams, U. of Memphis Matt Hayat, Georgia State U. Sarai Hedges, U. of Cincinnati Jacqueline Herman, Northern Kentucky U. Dawn Holmes, U. of California Santa Barbara Yi-Chun Hong, Arizona State Univ Tisha Hooks, Winona State U. Nicholas Horton, Amherst College Leanna House, Virginia Tech Jingchen (Monika) Hu, Vassar College Patricia Humphrey, Georgia Southern U. Debra Hydorn, U. of Mary Washington","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"288 - 289"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1693194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41352353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1677533
L. Lesser, D. Pearl, J. J. Weber, Dominic M. Dousa, R. Carey, Stephen A. Haddad
Abstract This article describes the process used to develop and assess an NSF-funded instructional innovation: an online collection (https://www.CAUSEweb.org/smiles/) of 28 interactive songs of high esthetic quality designed to span literature-based learning objectives of introductory statistics that develop statistical literacy and reasoning. The interactive songs are also designed to reduce statistics anxiety and require little time or instructor expertise. The songs are interactive in that the interface solicits (and provides hints and feedback on) student contributions (concepts or examples) and then plays back the song with student inputs integrated and highlighted. After providing a brief background, this article describes requirements, challenges, and opportunities in educational songwriting for the mathematical sciences, then describes the intervention and how its special nature affected the development process. Pilot studies at a research university and at a majority Black two-year college showed that students found the innovation to be a good tool to help their learning, reduce their anxiety about statistics, have an easy to follow interface, and use high quality songs. Analysis of log files from the use of the software shows some evidence of better performance on assessments after use and informs improvements of the automated feedback. Supplemental materials for this article are available online.
{"title":"Developing Interactive Educational Songs for Introductory Statistics","authors":"L. Lesser, D. Pearl, J. J. Weber, Dominic M. Dousa, R. Carey, Stephen A. Haddad","doi":"10.1080/10691898.2019.1677533","DOIUrl":"https://doi.org/10.1080/10691898.2019.1677533","url":null,"abstract":"Abstract This article describes the process used to develop and assess an NSF-funded instructional innovation: an online collection (https://www.CAUSEweb.org/smiles/) of 28 interactive songs of high esthetic quality designed to span literature-based learning objectives of introductory statistics that develop statistical literacy and reasoning. The interactive songs are also designed to reduce statistics anxiety and require little time or instructor expertise. The songs are interactive in that the interface solicits (and provides hints and feedback on) student contributions (concepts or examples) and then plays back the song with student inputs integrated and highlighted. After providing a brief background, this article describes requirements, challenges, and opportunities in educational songwriting for the mathematical sciences, then describes the intervention and how its special nature affected the development process. Pilot studies at a research university and at a majority Black two-year college showed that students found the innovation to be a good tool to help their learning, reduce their anxiety about statistics, have an easy to follow interface, and use high quality songs. Analysis of log files from the use of the software shows some evidence of better performance on assessments after use and informs improvements of the automated feedback. Supplemental materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"238 - 252"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1677533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44243368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1669508
Kenneth M. Brown
Abstract Over the past 30 years, the way in which the first course in statistics has been taught has moved away from a mathematics mode to take account of the distinctiveness of statistics. This article considers exercises for the typical introductory course and gives some evidence that many of these look like somewhat expanded versions of mathematics course exercises, and are thus less than optimal given the goals of statistics education. The article contends that exercises for statistics courses can be and should be designed with many interrelated questions built around a context to emphasize the connections between the tools used and the context. This type of exercise is termed a guided inquiry exercise (GIE) and it is argued that such exercises are not a new idea, are employed in some teaching materials, and are able to be created by instructors. Some of the implications and challenges for teaching and learning using GIEs are discussed. Teachers of statistics are encouraged to create their own, and advice is given to that end.
{"title":"More Questions and Fewer Contexts: Designing Exercises for Statistics Courses","authors":"Kenneth M. Brown","doi":"10.1080/10691898.2019.1669508","DOIUrl":"https://doi.org/10.1080/10691898.2019.1669508","url":null,"abstract":"Abstract Over the past 30 years, the way in which the first course in statistics has been taught has moved away from a mathematics mode to take account of the distinctiveness of statistics. This article considers exercises for the typical introductory course and gives some evidence that many of these look like somewhat expanded versions of mathematics course exercises, and are thus less than optimal given the goals of statistics education. The article contends that exercises for statistics courses can be and should be designed with many interrelated questions built around a context to emphasize the connections between the tools used and the context. This type of exercise is termed a guided inquiry exercise (GIE) and it is argued that such exercises are not a new idea, are employed in some teaching materials, and are able to be created by instructors. Some of the implications and challenges for teaching and learning using GIEs are discussed. Teachers of statistics are encouraged to create their own, and advice is given to that end.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"216 - 224"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1669508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45950555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1688595
C. Ashley, T. Bradstreet, Ruth Carver, Patrick Chen, Jaki Fesq, C. Franklin, R. Gould, Jeff Haberstroh, Brad Hartlaub, J. Holcomb, D. Joyce, L. Lesser, Jack Miller, Tom Moore, Jerry L. Moreno, R. Peck, Allan Rossman, Josh Tabor, Doug Tyson, J. Witmer
{"title":"“Short Stories”: Reflections on Tom Short’s Impact on Statistics Education","authors":"C. Ashley, T. Bradstreet, Ruth Carver, Patrick Chen, Jaki Fesq, C. Franklin, R. Gould, Jeff Haberstroh, Brad Hartlaub, J. Holcomb, D. Joyce, L. Lesser, Jack Miller, Tom Moore, Jerry L. Moreno, R. Peck, Allan Rossman, Josh Tabor, Doug Tyson, J. Witmer","doi":"10.1080/10691898.2019.1688595","DOIUrl":"https://doi.org/10.1080/10691898.2019.1688595","url":null,"abstract":"","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"138 - 146"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1688595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48184304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1702430
J. Witmer
{"title":"Note From the Editor","authors":"J. Witmer","doi":"10.1080/10691898.2019.1702430","DOIUrl":"https://doi.org/10.1080/10691898.2019.1702430","url":null,"abstract":"","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"135 - 135"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1702430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41788865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.1080/10691898.2019.1702415
J. Witmer
JSE has not published editorials in the past, just Notes from the Editor about the current issue of the journal, but I want to change that and introduce occasional editorials. Here is a first installment, which is a position paper on the use of pvalues, statistical inference, terminology, and related ideas. I thank JSE associate editors for their input, particularly Bill Notz who helped edit what follows.
{"title":"Editorial","authors":"J. Witmer","doi":"10.1080/10691898.2019.1702415","DOIUrl":"https://doi.org/10.1080/10691898.2019.1702415","url":null,"abstract":"JSE has not published editorials in the past, just Notes from the Editor about the current issue of the journal, but I want to change that and introduce occasional editorials. Here is a first installment, which is a position paper on the use of pvalues, statistical inference, terminology, and related ideas. I thank JSE associate editors for their input, particularly Bill Notz who helped edit what follows.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"136 - 137"},"PeriodicalIF":2.2,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1702415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45586940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：