Teaching Statistics最新文献

{"title":"Improving the students' learning process through the use of statistical applets","authors":"A. Variyath, Tharshanna Nadarajah","doi":"10.1111/test.12290","DOIUrl":"https://doi.org/10.1111/test.12290","url":null,"abstract":"Undergraduate statistics teaching has always faced the challenge of improving the learning quality on a continuous basis. Interactive statistical applets can enhance statistical knowledge by providing multiple representations of basic concepts and facilitating experimentation. The use of these applets will simplify the efforts for teaching statistics, especially in convincing students of the usability of statistics and facilitating quick learning in undergraduate courses. We developed and implemented a set of web‐based statistical applets from the following areas: Basic Statistics, Coin Toss App, Scatterplot‐Regression Line, Standard Normal Distribution, Normal Distribution, Histogram, Histogram (Case Examples), and Sampling ‐ Canada Map. These interactive applets can perform specific statistical tasks to improve the students' learning process.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43666740","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":"Is there a right way round?","authors":"H. MacGillivray","doi":"10.1111/test.12288","DOIUrl":"https://doi.org/10.1111/test.12288","url":null,"abstract":"At the recent OZCOTS (Australian Conference on Teaching Statistics), https://anzsc2021.com.au/ozcots-conference/, Rob Gould's keynote, titled Data Education in pre-College: promises and challenges, attracted a question from Matthew Parry, University of Otago, as to whether the scenario of “.. here's a bunch of data, come up with questions..” is a type of reversal of much previous advocacy to source or collect data to investigate identified issues. Rob's reply, and his discussion in his 2021 paper “Towards data-scientific thinking” [1], include comments that whatever codification is used for the statistical investigation cycle, now often called the data cycle or the learning from data cycle “...it is expected that investigators will ‘skip around' to some extent.” and that the order is not strict. This can be seen in examination of a variety of statistical and data investigations in real and complex contexts, whether in research or applications. In References [1,3], both Rob Gould and Andee Rubin emphasize “consider data” to include all aspects of the assembly of data, whether the data is assembled through sourcing, searching, collating or collecting, or is already available. They, and other authors, comment that the deluge of data means that students and indeed investigators more and more consider or access data already collected. Technological advances also enable greater and more ready access to collected data, and the necessary wrangling to handle such data. These in turn open up many possibilities for students to explore civic issues, including the critiquing of data with the associated vital learning about data quality and inherent dangers in uncritical algorithmic approaches. Rob also commented that students seem to find difficulty in identifying what statistical questions can be posed for an existing dataset. It is interesting to consider that today's data deluges require a return to more emphasis on the questions of “what, when, how, why, who?” In previous eras when instructors had no choice but to provide data and their context to students, these questions were of paramount importance in authentic statistical learning. For those in workplaces, not being able to find answers to such dataquerying questions, prevented the critiquing of reports or the building on previous data investigations or the redoing of analyses. As access to technology increased, enabling students to explore and analyse data beyond simplistic pocket calculator restrictions, students were able to design, collect, observe or source their own data to investigate issues involving a number of variables of interest to them. This could also introduce another question of great practical importance in many disciplines and workplaces, namely, can we measure what we want to measure? Including the information on the “what, when, why, how” in their reporting of data investigations, was, and is, excellent grounding for their future work whether in industry, business or research. Hen","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49148346","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":"Issue Information","authors":"","doi":"10.1111/test.12227","DOIUrl":"https://doi.org/10.1111/test.12227","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48327877","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":"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":"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":null,"pages":null},"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":"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":null,"pages":null},"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":"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":null,"pages":null},"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":"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":null,"pages":null},"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}