Traditional statistics education has focused on data from random samples and has capitalized on knowledge about a sample to understand an unknown population. However, many ubiquitous forms of data in the modern world do not clearly fit the sample-population assumptions that underpin statistical reasoning. For example, data collected in real time (eg, GPS, live traffic, tweets), image based (eg, photographs, drawings, facial recognition), semistructured (eg, data scraped from social media posts), repurposed (eg, school testing data to estimate housing prices), and big data (open access internet data, civic databases) are all examples of nontraditional data. Traditional data and data sources are typically simpler in nature, static, and more structured, whereas nontraditional forms of data are large, messy, complex, semistructured, or unstructured, constantly changing or evolving, and come in many different formats. While nontraditional forms of data and reasoning about uncertainty have been with us for some time [3,7,10], the digital age has led to a pervasive culture of data in all aspects of life, including those of our students. Widespread availability and access to myriad nonconventional, repurposed, massive, or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of, model, analyze, and make predictions from these data. New research directions have emerged, focusing on methods for making predictions from open, semirelated, and ubiquitous data, often relying heavily on computational methods and predictive modeling. Concerns have been expressed about the relative lack of attention to how and why data were collected, whether inferences being made are trustworthy and how statistics education might respond (eg, [18]). We are united in our goal to develop learners' deep understanding and reasoning with data and models. Therefore, awareness of the implications of nontraditional data—including complexities resulting from the contexts in which data are generated—has resulted in multiple discussions about how the field of statistics education may proceed (eg, [1,4,6,11,13]), but many questions remain open. This special issue addresses some of the open questions in how the field of statistics education may begin to support the teaching and learning of methods for working with nontraditional data. The articles in this issue focus on new approaches to the teaching and learning of data practices related to messy, complex, or nontraditional data from the youngest learners [8,19] to secondary learners [14,17], undergraduate students [15,16], graduate students, teachers, and researchers [2,5,9,17]. There are two overarching themes in the articles in this special issue: new ways to consider data visualizations in the classroom [2,5,14,17,19] and new approaches or elements that need to be considered in the teaching and learning of data science practices [8,9,15,16].
{"title":"Introduction to rethinking learners' reasoning with nontraditional data","authors":"J. Noll, S. Kazak, Lucía Zapata-Cardona, K. Makar","doi":"10.1111/test.12350","DOIUrl":"https://doi.org/10.1111/test.12350","url":null,"abstract":"Traditional statistics education has focused on data from random samples and has capitalized on knowledge about a sample to understand an unknown population. However, many ubiquitous forms of data in the modern world do not clearly fit the sample-population assumptions that underpin statistical reasoning. For example, data collected in real time (eg, GPS, live traffic, tweets), image based (eg, photographs, drawings, facial recognition), semistructured (eg, data scraped from social media posts), repurposed (eg, school testing data to estimate housing prices), and big data (open access internet data, civic databases) are all examples of nontraditional data. Traditional data and data sources are typically simpler in nature, static, and more structured, whereas nontraditional forms of data are large, messy, complex, semistructured, or unstructured, constantly changing or evolving, and come in many different formats. While nontraditional forms of data and reasoning about uncertainty have been with us for some time [3,7,10], the digital age has led to a pervasive culture of data in all aspects of life, including those of our students. Widespread availability and access to myriad nonconventional, repurposed, massive, or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of, model, analyze, and make predictions from these data. New research directions have emerged, focusing on methods for making predictions from open, semirelated, and ubiquitous data, often relying heavily on computational methods and predictive modeling. Concerns have been expressed about the relative lack of attention to how and why data were collected, whether inferences being made are trustworthy and how statistics education might respond (eg, [18]). We are united in our goal to develop learners' deep understanding and reasoning with data and models. Therefore, awareness of the implications of nontraditional data—including complexities resulting from the contexts in which data are generated—has resulted in multiple discussions about how the field of statistics education may proceed (eg, [1,4,6,11,13]), but many questions remain open. This special issue addresses some of the open questions in how the field of statistics education may begin to support the teaching and learning of methods for working with nontraditional data. The articles in this issue focus on new approaches to the teaching and learning of data practices related to messy, complex, or nontraditional data from the youngest learners [8,19] to secondary learners [14,17], undergraduate students [15,16], graduate students, teachers, and researchers [2,5,9,17]. There are two overarching themes in the articles in this special issue: new ways to consider data visualizations in the classroom [2,5,14,17,19] and new approaches or elements that need to be considered in the teaching and learning of data science practices [8,9,15,16].","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47253309","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}
Our complex world requires multivariate reasoning to make sense of reality. Within this paper, we offer a sequence of activities designed to develop multivariate reasoning by explicitly connecting data and visualization. The activities were designed based on a hypothetical learning trajectory we conjectured for students with limited experience with multivariate visualizations. Drawing from evidence collected using these activities in a series of professional development sessions with in‐service teachers, we find that the activities functioned as intended, and thus we promote these activities for developing students' multivariate reasoning at the secondary and post‐secondary level. We detail specific challenges the teachers faced, and based on these results, offer our reflections and recommendations for curricula and teaching.
{"title":"Designing a sequence of activities to build reasoning about data and visualization","authors":"V. Rao, Chelsey Legacy, A. Zieffler, R. delMas","doi":"10.1111/test.12341","DOIUrl":"https://doi.org/10.1111/test.12341","url":null,"abstract":"Our complex world requires multivariate reasoning to make sense of reality. Within this paper, we offer a sequence of activities designed to develop multivariate reasoning by explicitly connecting data and visualization. The activities were designed based on a hypothetical learning trajectory we conjectured for students with limited experience with multivariate visualizations. Drawing from evidence collected using these activities in a series of professional development sessions with in‐service teachers, we find that the activities functioned as intended, and thus we promote these activities for developing students' multivariate reasoning at the secondary and post‐secondary level. We detail specific challenges the teachers faced, and based on these results, offer our reflections and recommendations for curricula and teaching.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"S80 - S92"},"PeriodicalIF":0.8,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46452161","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}
Teaching statistics to generalist students oriented toward a profession, rather than academic merits, may be challenging. As statistics courses also tend to have a low student appeal, tailoring a course toward this type of audience is demanding. Framed within the theory of statistical thinking and literacy, this article shows how an investigative process, using domain data and real‐life examples, may facilitate meaningful learning and motivate students. Describing and reflecting upon the methods used, both in teaching and assessment, the article contributes to the practice of teaching statistics.
{"title":"Motivation for learning statistics: An example from fishery and aquaculture science","authors":"Signe A. Sønvisen","doi":"10.1111/test.12334","DOIUrl":"https://doi.org/10.1111/test.12334","url":null,"abstract":"Teaching statistics to generalist students oriented toward a profession, rather than academic merits, may be challenging. As statistics courses also tend to have a low student appeal, tailoring a course toward this type of audience is demanding. Framed within the theory of statistical thinking and literacy, this article shows how an investigative process, using domain data and real‐life examples, may facilitate meaningful learning and motivate students. Describing and reflecting upon the methods used, both in teaching and assessment, the article contributes to the practice of teaching statistics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"85 - 99"},"PeriodicalIF":0.8,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43646408","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}
L. Bosman, Esteban A. Soto, Thaís Ferraz Varela, Ebisa D. Wollega
Statistical knowledge is required for students in a range of disciplines. However, there are limited educator resources that exist for applying statistics to solve real‐world problems. This investigation provides one approach to teaching statistics using entrepreneurial‐minded learning (as a way to connect real‐world applications and value creation with problem‐solving and curiosity) in the context of solar energy. Both the ready‐to‐use teaching intervention and assessment of student learning details are provided for an undergraduate course on Introductory Statistics. The teaching intervention includes a series of seven lesson plans (and three extension projects) that educators can use in an introductory statistics course.
{"title":"Integrating the entrepreneurial mindset into solar energy statistical analysis and performance modeling","authors":"L. Bosman, Esteban A. Soto, Thaís Ferraz Varela, Ebisa D. Wollega","doi":"10.1111/test.12335","DOIUrl":"https://doi.org/10.1111/test.12335","url":null,"abstract":"Statistical knowledge is required for students in a range of disciplines. However, there are limited educator resources that exist for applying statistics to solve real‐world problems. This investigation provides one approach to teaching statistics using entrepreneurial‐minded learning (as a way to connect real‐world applications and value creation with problem‐solving and curiosity) in the context of solar energy. Both the ready‐to‐use teaching intervention and assessment of student learning details are provided for an undergraduate course on Introductory Statistics. The teaching intervention includes a series of seven lesson plans (and three extension projects) that educators can use in an introductory statistics course.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"79 - 84"},"PeriodicalIF":0.8,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49294426","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}
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
{"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":"79 4","pages":""},"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":" ","pages":""},"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}
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
{"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":"9 1","pages":"0"},"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}
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.
{"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":"45 1","pages":"69 - 78"},"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}
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