Teaching Statistics最新文献

{"title":"16th International Conference of The Mathematics Education for the Future Project: Building on the Past to Prepare for the Future","authors":"Announc Ement","doi":"10.1111/test.12289","DOIUrl":"https://doi.org/10.1111/test.12289","url":null,"abstract":"The Mathematics Education for the Future Project was founded in 1986 as an educational, noncommercial and philanthropic initiative to encourage and promote innovation in mathematics, science, statistics and computer education. Since 1999 we have held 15 international conferences which were renowned for their friendly and productive atmosphere and were attended by many movers and shakers in education world-wide.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43097797","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}

{"title":"Poll position: A sampling of statistical edutainment","authors":"D. Pearl, L. Lesser","doi":"10.1111/test.12284","DOIUrl":"https://doi.org/10.1111/test.12284","url":null,"abstract":"Jokes, cartoons, songs, videos, and quotes can be useful ways to engage students in discussion and learning key concepts about polling.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"124 - 128"},"PeriodicalIF":0.8,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48623139","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}

{"title":"Discovering experimental design: An interactive teaching exercise using Fisher's tea‐tasting experiment","authors":"T. Fanshawe","doi":"10.1111/test.12287","DOIUrl":"https://doi.org/10.1111/test.12287","url":null,"abstract":"An appreciation of experimental design is an important aspect of introductory statistics teaching in a wide range of applied disciplines, including medical statistics. Understanding the impact of design decisions on the choice of analysis method and subsequent interpretation of results can help to embed the importance of statistical thinking in the experimental process. I discuss an interactive exercise, based on R.A. Fisher's celebrated “Lady Tasting Tea” experiment, that is intended to raise awareness of design issues as part of an undergraduate statistics module. The exercise used a discovery approach, with students encouraged to identify design issues and agree on solutions themselves via small group discussion, with only low‐level prompting from the instructor. The value of this teaching style and possible extensions of the tea‐tasting experiment to other related topics suitable for more widespread use are also discussed.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"140 - 145"},"PeriodicalIF":0.8,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41826510","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}

{"title":"A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data","authors":"Daniel Frischemeier, Rolf Biehler, Susanne Podworny, Lea Budde","doi":"10.1111/test.12283","DOIUrl":"https://doi.org/10.1111/test.12283","url":null,"abstract":"In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S182 - S189"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46814210","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}

{"title":"Preface","authors":"H. MacGillivray, J. Ridgway, Robert d. Gould","doi":"10.1111/test.12282","DOIUrl":"https://doi.org/10.1111/test.12282","url":null,"abstract":"Since 1979, Teaching Statistics has aimed to emphasize good practice in teaching statistical thinking in any context, whether in statistics or in other disciplines such as economics and business, biology and health sciences, engineering and information technology, psychology, mathematics, and any area which uses statistics. Teaching Statistics seeks to inform, enlighten, stimulate, guide, correct, inspire, entertain, and encourage. The Teaching Statistics Trust was established to publish it, and it arose from the International Statistical Education newsletter for International Statistical Institute (ISI) members. Other initiatives from the ISI's Education Committee, established in 1948, led, at the same time, to the first International Conference on Teaching Statistics (ICOTS) in 1982, and the committee itself became the International Association for Statistical Education in 1992. Statistics emerged in the 1700s as a discipline focused on collecting data to describe the demographic and economic situation of the state, as the basis for political action. In the mid-1800s and early 1900s, there was a coming together of people from very varied backgrounds, intent on solving practical problems (eg, associated with economics, health, weather, and the human condition), the emergence of statistical societies, and a creative blossoming of mathematical models (often associated with inference). However, too many curricula have set these early models in aspic and simply demand technical mastery from students, with no concern for working with authentic data or modeling per se. The core ideas of multidisciplinarity, addressing important problems, inventing models, and proposing actions, have been displaced by the pursuit of decontextualized mathematical skills. Data science is a wake-up call to retrieve the heritage of statistics. As long advocated by professional statisticians and leading statistical educators, good practice in teaching statistics should reflect the practice of statistics in the fullest sense of “greater statistics” as described by Chambers in 1993 [1] and data analysis as described by Tukey in 1962 [4], integrating principles of data investigations with statistical literacy for all, as described by many including Rumsey in 2002 [3]:","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48445611","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}

{"title":"Building employability capabilities in data science students: An interdisciplinary, industry‐focused approach","authors":"Sonia Ferns, A. Phatak, S. Benson, Nina Kumagai","doi":"10.1111/test.12272","DOIUrl":"https://doi.org/10.1111/test.12272","url":null,"abstract":"In the contemporary workplace, data scientists who are capable of interdisciplinary collaboration are in high demand. Universities need to provide data science students with a plethora of learning opportunities that involve collaboration in interdisciplinary contexts and engagement with industry partners. Curtin University and Lab Tests Online Australasia (LTOAU) collaborated to provide an interdisciplinary, industry‐focused learning experience for data science students. Upon completing the project, students reported improved understanding of the range of applications for data science skills. The experience delivered opportunities for greater self‐awareness and highlighted the importance of teamwork, decision‐making and leadership skills. This chapter presents Interdisciplinary Project‐based Work‐Integrated Learning (IPjWIL), an educational approach that equips data science students with the necessary skills to navigate the future world of work. The results of the pilot project described demonstrate how interdisciplinary, industry‐focused learning experiences enhance the capabilities of data science students, thereby augmenting employability.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S216 - S225"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47820937","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}

{"title":"A classroom‐ready activity on educational disparities in the United States","authors":"Melody Wilson, Andrew Ross, Stephanie A. Casey","doi":"10.1111/test.12252","DOIUrl":"https://doi.org/10.1111/test.12252","url":null,"abstract":"We present a classroom‐ready activity for high school or college students involving an investigation of a rich, multivariate data set concerning educational and demographic characteristics of K‐12 schools at the state level in the United States. The data set includes educational characteristics such as per‐pupil revenue and graduation rate along with demographic characteristics such as poverty rate and economic segregation. Analysis of the data set sparks conversations regarding educational disparities for students in the United States and could prompt curiosity about educational disparities in other locations as well.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S93 - S97"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44217218","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}

{"title":"Statistics education from a data‐centric perspective","authors":"Matthias Gehrke, Tanja Kistler, Karsten Lübke, Norman Markgraf, Bianca Krol, Sebastian Sauer","doi":"10.1111/test.12264","DOIUrl":"https://doi.org/10.1111/test.12264","url":null,"abstract":"The ubiquitous acquisition and generation of data require a reworking of curricula in introductory statistics in tertiary education. We present a renewed curriculum that focuses on scientific thinking, modeling, and simulation‐based inference, utilizing R and various R tools such as shiny and learnr apps. We teach statistics from a data‐centric perspective, enabling the students to become data literate. Initial feedback of students and educators at our university of applied sciences shows that students' conceptual understanding improved and they understood the practical applicability of statistics better.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S201 - S215"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47863323","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}

{"title":"Teaching statistics and data science in England's schools","authors":"N. Davies, N. Sheldon","doi":"10.1111/test.12276","DOIUrl":"https://doi.org/10.1111/test.12276","url":null,"abstract":"In 2003, Holmes [The Statistician 52, Part 4, (2003), 439–474] reviewed 50 years of teaching statistics in English schools and drew out many lessons for more effective teaching. We consider the current relevance of several of these lessons and whether we have learned from them. Many national reports have been published about teaching statistics with few of their recommendations being implemented. There have been rapid developments in the methodology of, and technology used by, statistics at all levels outside schools. These have led to the accelerated development of the use of statistics and related disciplines such as data science (DS). In schools, we have not seen as much progress in teaching, learning, assessing, or communicating these subjects. We consider reasons for lack of progress in pedagogy and make our own recommendations to get schools to catch up with the exciting developments in the practice and communication of statistics and DS.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S52 - S70"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44132125","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}

{"title":"Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks","authors":"Rolf Biehler, Yannik Fleischer","doi":"10.1111/test.12279","DOIUrl":"https://doi.org/10.1111/test.12279","url":null,"abstract":"This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary‐school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's “Arbor” plug‐in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu‐based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"43 1","pages":"S133 - S142"},"PeriodicalIF":0.8,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45502494","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}