Teaching Statistics最新文献

{"title":"Issue Information","authors":"","doi":"10.1002/kjm2.12732","DOIUrl":"https://doi.org/10.1002/kjm2.12732","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41368691","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":"What goes before the CART? Introducing classification trees with Arbor and CODAP","authors":"Tim Erickson, J. Engel","doi":"10.1111/test.12347","DOIUrl":"https://doi.org/10.1111/test.12347","url":null,"abstract":"This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build‐it‐yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree‐building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45198100","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 possibilities of exploring nontraditional datasets with young children","authors":"Lucía Zapata-Cardona","doi":"10.1111/test.12349","DOIUrl":"https://doi.org/10.1111/test.12349","url":null,"abstract":"Today's world is characterized by the extensive production of data in different scenarios that everyday citizens need to understand for their informed participation in society. With the increase in the availability of data in a society defined by the industrious production of data, the educational system needs to think of possibilities to bring young children closer to the world of data science. This paper presents a nontraditional data exploration experience with an 8‐year‐old participant helped by a data visualization tool. A task‐based interview was conducted while the participant explored a carbon dioxide emission dataset. This paper studied how the participant interrogates the data, draws inferences and exhibits dispositions. At the end, some reflections are presented when introducing the exploration of nontraditional data in teaching.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43640408","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":"M in CoMputational thinking: How long does it take to read a book?","authors":"Kym Fry, K. Makar, J. Hillman","doi":"10.1111/test.12348","DOIUrl":"https://doi.org/10.1111/test.12348","url":null,"abstract":"Even at the primary level, computational thinking (CT) can support young students to prepare for participating in futures that are immersed in data. In mathematics classrooms, there are few explanations of the ways CT can support students in formulating and solving complex problems. This paper presents an example of a primary classroom investigation (8‐9 year olds) over seven lessons of the problem “How long does it take to read a book?” The aim is to illustrate ways a statistical investigation can provide context for CT and demonstrate how the two complement each other to solve problems involving mathematics. The findings highlight opportunities and challenges that students face across the elements of CT—decomposition, abstraction, pattern recognition and modelling, and generalization and algorithmic thinking, including recommendations for teaching.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42538853","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":"Reflections on gaze data in statistics education","authors":"L.B.M.M. Boels","doi":"10.1111/test.12340","DOIUrl":"https://doi.org/10.1111/test.12340","url":null,"abstract":"Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye‐tracking research as an example, background information on eye‐tracking and possible applications of eye‐tracking in statistics education is provided. Teachers indicated that our eye‐tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45337642","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":"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"title":"International Association for Statistical Education (IASE)","authors":"Larry Weldon, Carmen Batanero","doi":"10.1111/test.12337","DOIUrl":"https://doi.org/10.1111/test.12337","url":null,"abstract":"Oxford, UK TEST eaching Statistics 0141-982X © Teaching Sta stics Trust 2004 171 riginal Article Editorial Address: Andrej Blejec, National Institute of Biology, Vecna pot 111 POB 141, SI-1000 Ljubljana, Slovenia. Tel: +386-1-423-33-88, Fax: +386-1-2412-980 E-mail: andrej.blejec@nib.si and K. Laurence Weldon, Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 Tel: +1-604-291-3667, Fax: +1-604-291-4368 E-mail: weldon@sfu.ca","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":"44724711","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}