Teaching Statistics最新文献

{"title":"Issue Information","authors":"","doi":"10.1111/test.12310","DOIUrl":"https://doi.org/10.1111/test.12310","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42517420","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":"Choosing and using data and contexts for learning","authors":"H. MacGillivray","doi":"10.1111/test.12338","DOIUrl":"https://doi.org/10.1111/test.12338","url":null,"abstract":"The value for student statistical learning and engagement of real data and real contexts has been stated and established for so long and so thoroughly by statisticians and statistics educators that it is almost superfluous to restate it. Over the last twenty-five years, there has also been increasing emphasis on the importance of using complex real data. Initially the emphasis was in terms of data with a number of variables, whether the data are to be collected by students or accessed, but alongside rapidly growing technological advances and their incorporation in teaching statistics, advocacy for complexity has broadened to encompass large datasets, many variables, datasets already collected that need wrangling or “treatment” before teaching, or non-traditional data. In parallel with emphasis on real data and contexts, there has also been advocacy on critiquing reports on data and data-based commentary and analysis in the media or in accounts in other disciplines. Real, complex data and real contexts provide opportunities for rich and authentic statistical learning, but are not without a range of challenges and the need for careful thought and sound expertise in statistics and its learning for those teaching statistics, especially at the introductory level. Often discussion of teacher planning and pedagogy focuses on the data and its nature, but challenges can also feature significantly in context, required curricula and externally-imposed constraints on student time and assessments. Contexts in particular require careful selection and thought, especially at foundation and introductory level. Contexts must be readily accessible to the relevant student cohort so that they provide a suitable vehicle for statistical learning. If a context requires more than basic understanding from students or if a context is too dominant, authentic statistical learning is inhibited by context learning or by non-transferability of learning. In designing learning experiences, learning purpose embeds content, pedagogical structure and external constraints, the last of which can be considerably restrictive at school, tertiary or workplace levels. Good context and data choice must therefore take account of the student cohort in regard to both prior and current learning, and discipline situation. All those who have been involved in choosing contexts and datasets know how much work is involved in preparation of them for student use, even for extra-curricular open-ended investigations without curricula and assessment restrictions. Preparing good classroom-ready learning resources within a given curriculum requires significant statistical and teaching expertise. Those involved in teaching statistics into other disciplines at tertiary level know the diplomacy and combined knowledge of students and statistics required to balance the desires and demands of other disciplines, as well as students' and institutional restrictions on time and assessments. Contexts with datasets su","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41284523","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":"The Mathematics Education for the Future Project","authors":"C. M. Bhaird","doi":"10.33232/BIMS.0086.12","DOIUrl":"https://doi.org/10.33232/BIMS.0086.12","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49481473","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":"The Mathematics Education for the Future Project","authors":"","doi":"10.1111/test.12336","DOIUrl":"https://doi.org/10.1111/test.12336","url":null,"abstract":"Teaching StatisticsVolume 45, Issue 2 p. 125-125 ANNOUNCEMENT The Mathematics Education for the Future Project First published: 19 April 2023 https://doi.org/10.1111/test.12336Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume45, Issue2Summer 2023Pages 125-125 RelatedInformation","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135464563","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 One‐Way ANOVA with engaging NBA data and R Shiny within a flexdashboard","authors":"Danielle Sisso, Nicole Bass, Immanuel Williams","doi":"10.1111/test.12332","DOIUrl":"https://doi.org/10.1111/test.12332","url":null,"abstract":"This paper provides introductory statistics instructors with the capacity to use engaging National Basketball Association (NBA) data within a web application to either strengthen students' understanding or introduce the concept of variance and one‐way analysis of variance. Using engaging data within the classroom provides context to data that students deem applicable to their lives. This paper not only provides a lesson plan for teaching these concepts but also provides a web application and the engaging NBA dataset if the instructor decides to use the app or the data in another context. The NBA data selected to focus on the debate “Who is the greatest NBA player of all time?”. By using context students are familiar with and interested in, we can get them interested in and further engaged in statistics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46469142","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":"Students' approaches to exploring relationships between categorical variables","authors":"Traci Higgins, J. Mokros, Andee Rubin, Jacob Sagrans","doi":"10.1111/test.12331","DOIUrl":"https://doi.org/10.1111/test.12331","url":null,"abstract":"In the context of an afterschool program in which students explore relatively large authentic datasets, we investigated how 11‐ to 14‐year old students worked with categorical variables. During the program, students learned to use the Common Online Data Analysis Platform (CODAP), a statistical analysis platform specifically designed for middle and high school students, to create and interpret graphs. Following the program, we conducted individual clinical interviews, during which students used CODAP to answer questions about relationships between variables. Here, we describe how students engaged in exploratory data analysis that involved looking at relationships between two categorical variables. Students worked from data in table form and created “contingency graphs,” a variant of contingency tables, which they used to analyze and draw insights from the data. Our research identified four strategies that students used to examine the data in order to explore patterns, make comparisons, and answer questions with the data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47455324","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":"Visual expression of factor decomposition in regression analysis: An example of Japanese housing rents","authors":"Kosei Fukuda","doi":"10.1111/test.12333","DOIUrl":"https://doi.org/10.1111/test.12333","url":null,"abstract":"This paper presents the importance of the visual expression of factor decomposition in regression analysis, which is particularly worthwhile for undergraduate students whose majors are not mathematics but social science. The conventional purpose of regression analysis is to examine specific hypotheses empirically. In particular, the statistical significance of the explanatory variable was tested, which may have been difficult for many students to understand mathematically. To remedy this, factor decomposition is introduced in the same way that human body composition is broken down into water, fat, and muscle. As an illustrative example, multiple regression was applied to the determinants of housing rents in Japan. The explanatory variables were the living area, building age, and walking time from the nearest station. The findings suggest that, with the help of visual expression, a student can easily appreciate which variable significantly affects housing rents.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48310202","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":"Students' articulations of uncertainty about big data in an integrated modeling approach learning environment","authors":"Ronit Gafny, D. Ben-Zvi","doi":"10.1111/test.12330","DOIUrl":"https://doi.org/10.1111/test.12330","url":null,"abstract":"In recent years, big data has become ubiquitous in our day‐to‐day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data—the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42343669","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":"How learners produce data from text in classifying clickbait","authors":"N. Horton, J. Chao, P. Palmer, W. Finzer","doi":"10.1111/test.12339","DOIUrl":"https://doi.org/10.1111/test.12339","url":null,"abstract":"Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task‐based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as “clickbait” or “news.” Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human‐perception level and the computer‐extraction level and conceptualizing connections between them.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46788213","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":"Peter Holmes Prize Announcement 2022","authors":"H. MacGillivray","doi":"10.1111/test.12328","DOIUrl":"https://doi.org/10.1111/test.12328","url":null,"abstract":"The article entitled “The ‘p-hacking-is-terrific’ ocean – a cartoon for teaching statistics” by Dinghan Guo and Yue Ma has been awarded the Peter Holmes prize for 2022 The aim of this prize is to highlight excellence in motivating practical classroom activity. This article describes using a cartoon depicting “going on a fishing expedition” to assist in classroom discussion, student discovery activity, awareness and understanding of the scientific dangers and potential mistakes in searching for evidence in the form of “statistical significance” to support a scientific hypothesis or claim. The article underscores the importance in teaching the understanding of fundamental statistical concepts and their responsible use, for all students no matter what their future, and in professional development and re-development for researchers in other disciplines. The article uses a conversational style to outline some ways in which the cartoon could be used with a set of trigger questions. Although it is not the type of cartoon that an instructor would just put up on the screen to get laughs and have a brief classroom discussion, it can be used at different educational levels, from senior school to postgraduate and workplace in other disciplines, to discuss and think about different levels of questions relevant to the teaching context and cohort. The core messages of the article, including the inevitability of eventually getting the outcome you want if you just keep trying, making assumptions as desired all the way, appear to be difficult to communicate even to experienced scientists, and the fishing analogy is direct while also allowing for diving into more complex underlying concepts if appropriate. With references to pertinent commentary from other disciplines, statisticians and statistical educators, the article demonstrates how a cartoon can capture attention, highlight an important problem in use and misuse of statistics in research, and be used to trigger questions and student exploration, enquiry and discussion at a level relevant to the teaching context and cohort. Overall, this article embodies the aim and spirit of the Peter Holmes prize in an excellent demonstration of a fun stimulus to trigger classroom discussion and student questions and enquiry, across disciplines and educational levels, in order to promote responsible use, and prevent or call out misuse, of some fundamental statistical concepts.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48114579","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}