Traci Higgins, J. Mokros, Andee Rubin, Jacob Sagrans
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.
{"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":"45 1","pages":"S52 - S66"},"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}
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.
{"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":"45 1","pages":"100 - 105"},"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}
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.
{"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":"45 1","pages":"S67 - S79"},"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}
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.
{"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":"45 1","pages":"S103 - S93"},"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}
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.
{"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":" ","pages":""},"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}
Robert C. Butler, Christopher D. Blair, Rae Ette Newman, Leah L Batchelor
This study compared the effectiveness of teaching a distance education statistics course using a computer‐aided personalized system of instruction (CAPSI) in comparison to a distance education course that used video lectures. Data were collected between 2017 and 2022. Two‐hundred and sixty‐eight students were included in the sample. Results supported that students enrolled in the CAPSI statistics course were less likely to drop out of the course and mastered significantly more material than students enrolled in the lecture‐based distance education course. It is recommended that instructors teaching statistics in distance education settings consider using CAPSI to improve student outcomes.
{"title":"Using a computer‐aided personalized system of instruction to enhance the mastery of statistics in online learning","authors":"Robert C. Butler, Christopher D. Blair, Rae Ette Newman, Leah L Batchelor","doi":"10.1111/test.12346","DOIUrl":"https://doi.org/10.1111/test.12346","url":null,"abstract":"This study compared the effectiveness of teaching a distance education statistics course using a computer‐aided personalized system of instruction (CAPSI) in comparison to a distance education course that used video lectures. Data were collected between 2017 and 2022. Two‐hundred and sixty‐eight students were included in the sample. Results supported that students enrolled in the CAPSI statistics course were less likely to drop out of the course and mastered significantly more material than students enrolled in the lecture‐based distance education course. It is recommended that instructors teaching statistics in distance education settings consider using CAPSI to improve student outcomes.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"40 1","pages":"148 - 157"},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63468934","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}
As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.
{"title":"Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics","authors":"J. Noll, Maria Tackett","doi":"10.1111/test.12345","DOIUrl":"https://doi.org/10.1111/test.12345","url":null,"abstract":"As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"S21 - S5"},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63468886","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 Special Issue will showcase work that was presented at SRTL-12. Many ubiquitous forms of data do not clearly fit the sample-population assumptions that underpin the statistical reasoning that has been the focus of much in statistical education. For example, data collected in real time (GPS, live traffic, tweets), image-based (photographs, drawings, facial recognition), semi-structured (scraped from social media posts), repurposed (school testing data to estimate housing prices) and big data (open access internet data, civic databases) are all examples of non-traditional data. While non-traditional forms of data have been with us for some time, 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 of non-conventional, repurposed, massive or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of and interrogate data as well as how they model, analyze and make predictions from these forms of data. This special issue focuses on empirical studies that investigate or nurture learners' understanding and reasoning with non-traditional, messy and/or complex data and models. Papers will focus on practical advice and implications for good practice in teaching statistics using non-traditional data.
{"title":"Announcement of Special Issue 2023 in Teaching Statistics","authors":"H. MacGillivray","doi":"10.1111/test.12326","DOIUrl":"https://doi.org/10.1111/test.12326","url":null,"abstract":"This Special Issue will showcase work that was presented at SRTL-12. Many ubiquitous forms of data do not clearly fit the sample-population assumptions that underpin the statistical reasoning that has been the focus of much in statistical education. For example, data collected in real time (GPS, live traffic, tweets), image-based (photographs, drawings, facial recognition), semi-structured (scraped from social media posts), repurposed (school testing data to estimate housing prices) and big data (open access internet data, civic databases) are all examples of non-traditional data. While non-traditional forms of data have been with us for some time, 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 of non-conventional, repurposed, massive or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of and interrogate data as well as how they model, analyze and make predictions from these forms of data. This special issue focuses on empirical studies that investigate or nurture learners' understanding and reasoning with non-traditional, messy and/or complex data and models. Papers will focus on practical advice and implications for good practice in teaching statistics using non-traditional data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43283940","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 article entitled “Characteristics of statistical literacy skills from the perspective of critical thinking” by Shunya Koga has been awarded the C. Oswald George prize for 2022 This paper describes the development of a framework to illustrate statistical literacy skills in terms of critical thinking, through investigating existing research on critical thinking, and examining and aligning its characteristics in the domain of statistical literacy across the range of its descriptions in statistical education research. The critical thinking concept is wide and diverse, and this study organizes the characteristics of critical thinking skills that are representative studies in philosophical research, by identifying their similarities and differences. The study examines how those skills are demonstrated in the context of statistical literacy as described in considerable existing research, for example in situations such as interpreting and critically evaluating statistical information. The critical thinking skills presented in this study are intended for adults or high school students and above. The article acknowledges the challenges in the many possible ways of investigating critical thinking skills in the teaching and assessing of statistical literacy, that is, in the implementation of the research descriptions in the practice of teaching and assessment. One difficulty is that curricula are not necessarily focused only on the characteristics of statistical literacy common across the various research descriptions of it. Here just one application of the developed framework of characterizations of critical thinking in the context of statistical literacy is considered, namely a course explicitly on statistical literacy. Assessment could be analyzed, but here some teaching materials, namely textbooks written for the course, are considered to illustrate the framework. By investigating, identifying, analysing, aligning and bringing together wide-ranging research work on statistical literacy and critical thinking skills, this paper provides thoughtful insight and a framework for investigating critical thinking skills in the teaching and assessing of statistical literacy, that is, in the implementation of the research descriptions in actual teaching and assessment. In doing so, the paper also implicitly indicates that investigation of critical thinking skills is needed into wider aspects of statistical thinking skills. Congratulations to the author for a thoughtful and challenging analysis and development.
Koga Shunya的题为“批判性思维视角下的统计素养技能特征”的文章已被授予2022年C.Oswald-George奖。本文通过调查现有的批判性思维研究，描述了从批判性思维角度说明统计素养技能的框架的发展，以及在统计教育研究中对其描述的范围内，审查和调整其在统计素养领域的特征。批判性思维概念是广泛而多样的，本研究通过识别它们的异同，组织了哲学研究中具有代表性的批判性思维技能的特征。这项研究考察了在大量现有研究中所述的统计素养背景下，例如在解释和批判性评估统计信息等情况下，如何展示这些技能。本研究中提出的批判性思维技能适用于成人或高中及以上学生。文章承认，在统计素养的教学和评估中，即在教学和评估实践中实施研究描述时，调查批判性思维技能的许多可能方法存在挑战。一个困难是，课程不一定只关注统计素养的特征，这些特征在各种研究描述中都很常见。这里只考虑了在统计素养的背景下应用批判性思维特征的一个发展框架，即明确的统计素养课程。评估可以进行分析，但在这里，一些教材，即为该课程编写的教科书，被认为是对框架的说明。通过调查、识别、分析、调整和整合关于统计素养和批判性思维技能的广泛研究工作，本文为调查统计素养教学和评估中的批判性思维技能提供了深思熟虑的见解和框架，在实际教学和评估中实施研究性描述。在这样做的过程中，论文还隐含地表明，需要对批判性思维技能进行更广泛的调查，以了解统计思维技能的各个方面。祝贺作者进行了深思熟虑、富有挑战性的分析和开发。
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