Pub Date : 2022-04-19DOI: 10.1080/26939169.2022.2054881
Michael Dalton, Randall E. Groth
Abstract The Journal of Statistics and Data Science Education (JSDSE) has a history of initiatives to expand its readership and impact. In this article, we reflect on JSDSE’s relevance to K-12 education and how it might increase its influence in this area. We characterize JSDSE’s K-12 impact partially in terms of its ability to foster statistical knowledge for teaching (SKT). We also introduce a new construct, statistical knowledge for the transformation of teaching (SKT , to more fully capture the K-12 contributions JSDSE can make. Our analysis draws upon the perspectives of an experienced high school teacher (the first author) and a statistics education researcher (the second author). The first author of the article surveyed recent JSDSE issues to identify and reflect upon articles with implications for his teaching practice. The second author framed the first author’s article reflections in terms of their connections to SKT and SKT2. Drawing upon our collaborative analysis, we propose strategies to help JSDSE more fully realize its potential to contribute to SKT and SKT2. We explain how acting on the proposed strategies may help JSDSE readers, authors, and editors bridge the persistent historical gap between formal scholarship and practice in K-12 education.
{"title":"Reflections on the Current and Potential K-12 Impact of the Journal of Statistics and Data Science Education","authors":"Michael Dalton, Randall E. Groth","doi":"10.1080/26939169.2022.2054881","DOIUrl":"https://doi.org/10.1080/26939169.2022.2054881","url":null,"abstract":"Abstract The Journal of Statistics and Data Science Education (JSDSE) has a history of initiatives to expand its readership and impact. In this article, we reflect on JSDSE’s relevance to K-12 education and how it might increase its influence in this area. We characterize JSDSE’s K-12 impact partially in terms of its ability to foster statistical knowledge for teaching (SKT). We also introduce a new construct, statistical knowledge for the transformation of teaching (SKT , to more fully capture the K-12 contributions JSDSE can make. Our analysis draws upon the perspectives of an experienced high school teacher (the first author) and a statistics education researcher (the second author). The first author of the article surveyed recent JSDSE issues to identify and reflect upon articles with implications for his teaching practice. The second author framed the first author’s article reflections in terms of their connections to SKT and SKT2. Drawing upon our collaborative analysis, we propose strategies to help JSDSE more fully realize its potential to contribute to SKT and SKT2. We explain how acting on the proposed strategies may help JSDSE readers, authors, and editors bridge the persistent historical gap between formal scholarship and practice in K-12 education.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"179 - 186"},"PeriodicalIF":1.7,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48721074","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}
Pub Date : 2022-04-04DOI: 10.1080/26939169.2022.2046522
Brandon J. George, Juan Leon
Abstract The recent rise in online learning in statistics has made it essential for instructors to teach effectively in that modality. In this retrospective, we reflect on how online course content was added to an introductory statistics course in a graduate public health program and how it was updated over time based on student feedback and instructor experiences. The impetus for the inclusion of online learning as well as the consequences of specific implementations are discussed for hybrid and fully online class formats. We found that while online learning seemed to be beneficial as a whole for public health students learning health statistics, identifiable differences in the design or implementation of lectures, software demonstrations, online discussion, computer lab activities, active learning, and homework assignments had a substantial effect on learning outcomes and student satisfaction.
{"title":"Making the Switch: Experiences and Results from Converting a Biostatistics Course to Flipped and Online Formats for Public Health Students","authors":"Brandon J. George, Juan Leon","doi":"10.1080/26939169.2022.2046522","DOIUrl":"https://doi.org/10.1080/26939169.2022.2046522","url":null,"abstract":"Abstract The recent rise in online learning in statistics has made it essential for instructors to teach effectively in that modality. In this retrospective, we reflect on how online course content was added to an introductory statistics course in a graduate public health program and how it was updated over time based on student feedback and instructor experiences. The impetus for the inclusion of online learning as well as the consequences of specific implementations are discussed for hybrid and fully online class formats. We found that while online learning seemed to be beneficial as a whole for public health students learning health statistics, identifiable differences in the design or implementation of lectures, software demonstrations, online discussion, computer lab activities, active learning, and homework assignments had a substantial effect on learning outcomes and student satisfaction.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"91 - 101"},"PeriodicalIF":1.7,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45072846","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}
Pub Date : 2022-03-31DOI: 10.1080/26939169.2022.2074582
L. Vilhuber, Hyuk Harry Son, Meredith Welch, D. Wasser, Michael Darisse
Abstract We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular activities. While the activity is not part of a regular curriculum, the skills and knowledge taught through explicit training of reproducible methods and principles, and reinforced through repeated application in a real-life workflow, contribute to the education of these undergraduate students, and prepare them for post-graduation jobs and further studies. Supplementary materials for this article are available online.
{"title":"Teaching for Large-Scale Reproducibility Verification","authors":"L. Vilhuber, Hyuk Harry Son, Meredith Welch, D. Wasser, Michael Darisse","doi":"10.1080/26939169.2022.2074582","DOIUrl":"https://doi.org/10.1080/26939169.2022.2074582","url":null,"abstract":"Abstract We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular activities. While the activity is not part of a regular curriculum, the skills and knowledge taught through explicit training of reproducible methods and principles, and reinforced through repeated application in a real-life workflow, contribute to the education of these undergraduate students, and prepare them for post-graduation jobs and further studies. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"274 - 281"},"PeriodicalIF":1.7,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44971263","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}
Pub Date : 2022-03-18DOI: 10.1080/26939169.2022.2039085
B. Mayer, Anja Kuemmel, Marianne Meule, R. Muche
Abstract Teaching practical skills is of particular interest in the study of human medicine. With regard to medical statistics this means the use of statistical software, which may be effectively taught by a flipped classroom approach. As a pilot study, we designed and implemented an elective course on medical statistics that focused on hands-on data analysis in SAS Studio. Students independently prepared for class using materials such as pretaped asynchronous lectures, and worked on exercises during synchronous class sessions. On course evaluations completed by n 15 students out of 26, students rated their satisfaction with the course a mean of 1.3 (SD 0.6) on a scale where 1 best and 6 worst. Twelve (80%) indicated that they processed all materials provided, and 11 students (73%) rated the frequency of direct contact with the instructor as sufficient. Nearly all (14 out of 15) viewed the course as an adequate substitute for a full face-to-face course. Our results suggest that the proposed course design is well-accepted. The flipped classroom format offers high flexibility and can be implemented easily online. Our pilot data are encouraging regarding the aim of designing a prospective follow-up study which compares the flipped classroom approach to a teaching format based on attendance.
{"title":"Introduction to Medical Statistics Software Using the Flipped Classroom: A Pilot Study","authors":"B. Mayer, Anja Kuemmel, Marianne Meule, R. Muche","doi":"10.1080/26939169.2022.2039085","DOIUrl":"https://doi.org/10.1080/26939169.2022.2039085","url":null,"abstract":"Abstract Teaching practical skills is of particular interest in the study of human medicine. With regard to medical statistics this means the use of statistical software, which may be effectively taught by a flipped classroom approach. As a pilot study, we designed and implemented an elective course on medical statistics that focused on hands-on data analysis in SAS Studio. Students independently prepared for class using materials such as pretaped asynchronous lectures, and worked on exercises during synchronous class sessions. On course evaluations completed by n 15 students out of 26, students rated their satisfaction with the course a mean of 1.3 (SD 0.6) on a scale where 1 best and 6 worst. Twelve (80%) indicated that they processed all materials provided, and 11 students (73%) rated the frequency of direct contact with the instructor as sufficient. Nearly all (14 out of 15) viewed the course as an adequate substitute for a full face-to-face course. Our results suggest that the proposed course design is well-accepted. The flipped classroom format offers high flexibility and can be implemented easily online. Our pilot data are encouraging regarding the aim of designing a prospective follow-up study which compares the flipped classroom approach to a teaching format based on attendance.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"74 - 79"},"PeriodicalIF":1.7,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41671855","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}
Pub Date : 2022-02-19DOI: 10.1080/26939169.2022.2138645
M. Dogucu, Mine Çetinkaya-Rundel
Abstract It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science instructors adopt reproducible workflows for their own teaching. We consider computational reproducibility, documentation, and openness as three pillars of reproducible teaching framework. We share tools, examples, and recommendations for the three pillars.
{"title":"Tools and Recommendations for Reproducible Teaching","authors":"M. Dogucu, Mine Çetinkaya-Rundel","doi":"10.1080/26939169.2022.2138645","DOIUrl":"https://doi.org/10.1080/26939169.2022.2138645","url":null,"abstract":"Abstract It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science instructors adopt reproducible workflows for their own teaching. We consider computational reproducibility, documentation, and openness as three pillars of reproducible teaching framework. We share tools, examples, and recommendations for the three pillars.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"251 - 260"},"PeriodicalIF":1.7,"publicationDate":"2022-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42278857","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}
Pub Date : 2022-01-02DOI: 10.1080/26939169.2022.2041325
N. Horton
{"title":"30 Years of the Journal of Statistics and Data Science Education","authors":"N. Horton","doi":"10.1080/26939169.2022.2041325","DOIUrl":"https://doi.org/10.1080/26939169.2022.2041325","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"1 - 2"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41986046","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}
Pub Date : 2022-01-02DOI: 10.1080/26939169.2022.2035286
Eric A. Vance, Jessica L. Alzen, Heather S. Smith
Abstract Statisticians and data scientists have been called upon to increase the impact they have through their collaborative projects. Statistics and data science practitioners and their educators can achieve and enable greater impact by learning how to create shared understanding with their collaborators as well as teaching this concept to their students, colleagues, and mentees. In this article, we explore and explain the concepts of common knowledge and shared understanding, which is the basis for action to accomplish greater impacts. We also explore related concepts of misunderstanding and doubtful understanding. We describe a process for teaching oneself and others how to create shared understanding. We conclude that incorporating the concept of shared understanding into one’s practice of statistics or data science and following the steps described will result in having more impact on projects and throughout one’s career.
{"title":"Creating Shared Understanding in Statistics and Data Science Collaborations","authors":"Eric A. Vance, Jessica L. Alzen, Heather S. Smith","doi":"10.1080/26939169.2022.2035286","DOIUrl":"https://doi.org/10.1080/26939169.2022.2035286","url":null,"abstract":"Abstract Statisticians and data scientists have been called upon to increase the impact they have through their collaborative projects. Statistics and data science practitioners and their educators can achieve and enable greater impact by learning how to create shared understanding with their collaborators as well as teaching this concept to their students, colleagues, and mentees. In this article, we explore and explain the concepts of common knowledge and shared understanding, which is the basis for action to accomplish greater impacts. We also explore related concepts of misunderstanding and doubtful understanding. We describe a process for teaching oneself and others how to create shared understanding. We conclude that incorporating the concept of shared understanding into one’s practice of statistics or data science and following the steps described will result in having more impact on projects and throughout one’s career.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"54 - 64"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41525515","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}
Pub Date : 2022-01-02DOI: 10.1080/26939169.2021.2008283
Brenna Curley, Anna D. Peterson
Abstract In this article, we outline several activities revolving around soccer players who participated in the 2018 FIFA World Cup and 2019 FIFA Women’s World Cup. Classroom activities are described from different perspectives, useful for a range of different statistics courses. In a first semester probability theory course, students investigate the counter-intuitive birthday paradox empirically and theoretically. For an introductory data science course, students practice their data wrangling skills using the statistical software R. Additional activities are shared for those instructors interested in emphasizing multivariable thinking in a second semester applied statistical modeling course. The activities shared will provide a range of opportunities for instructors to incorporate the FIFA World Cup data in their own courses. Supplementary files for this article are available online.
{"title":"A Fresh Shot at Statistics in the Classroom: Three Perspectives Using World Cup Soccer Player Data","authors":"Brenna Curley, Anna D. Peterson","doi":"10.1080/26939169.2021.2008283","DOIUrl":"https://doi.org/10.1080/26939169.2021.2008283","url":null,"abstract":"Abstract In this article, we outline several activities revolving around soccer players who participated in the 2018 FIFA World Cup and 2019 FIFA Women’s World Cup. Classroom activities are described from different perspectives, useful for a range of different statistics courses. In a first semester probability theory course, students investigate the counter-intuitive birthday paradox empirically and theoretically. For an introductory data science course, students practice their data wrangling skills using the statistical software R. Additional activities are shared for those instructors interested in emphasizing multivariable thinking in a second semester applied statistical modeling course. The activities shared will provide a range of opportunities for instructors to incorporate the FIFA World Cup data in their own courses. Supplementary files for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"86 - 98"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49411067","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}
Pub Date : 2022-01-02DOI: 10.1080/26939169.2022.2034488
Kimberlee Everson
Abstract This study aims to identify some perceived gaps in a selection of statistical skills and software abilities of professors of education in United States colleges and universities. In addition to a general U. S. sample, a sample of education professors in Historically Black Colleges and Universities (HBCUs) was examined in order to understand their unique needs. Results showed that many professors from both samples felt they were weak in their abilities with more advanced statistical methods such as structural equation modeling and propensity score matching. Professors of education at HBCUs, however, had significant perceived skill-need methodology gaps in most of the methodologies evaluated. The general U.S. sample indicated a skill-need gap with statistical software packages such as R, and the HBCU sample indicated a skill-need gap with all five software packages evaluated (Excel, SPSS, SAS, Stata, and R). Affordable training workshops addressing the greatest areas of perceived need should be helpful in reducing this skill-need gap.
{"title":"Statistical Skills Gaps of Professors of Education at U.S. Universities and HBCUs","authors":"Kimberlee Everson","doi":"10.1080/26939169.2022.2034488","DOIUrl":"https://doi.org/10.1080/26939169.2022.2034488","url":null,"abstract":"Abstract This study aims to identify some perceived gaps in a selection of statistical skills and software abilities of professors of education in United States colleges and universities. In addition to a general U. S. sample, a sample of education professors in Historically Black Colleges and Universities (HBCUs) was examined in order to understand their unique needs. Results showed that many professors from both samples felt they were weak in their abilities with more advanced statistical methods such as structural equation modeling and propensity score matching. Professors of education at HBCUs, however, had significant perceived skill-need methodology gaps in most of the methodologies evaluated. The general U.S. sample indicated a skill-need gap with statistical software packages such as R, and the HBCU sample indicated a skill-need gap with all five software packages evaluated (Excel, SPSS, SAS, Stata, and R). Affordable training workshops addressing the greatest areas of perceived need should be helpful in reducing this skill-need gap.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"45 - 53"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45902820","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}
Pub Date : 2022-01-02DOI: 10.1080/26939169.2021.2025177
Roger W. Johnson
Abstract For ease of instruction in the classroom, the one-way analysis of variance F statistic is rewritten in terms of pairwise differences in individual sample means instead of differences of individual sample means from the overall sample mean. Likewise, the Kruskal–Wallis statistic may be rewritten in terms of pairwise differences in individual average ranks rather than differences of individual average ranks from the overall average rank. In unbalanced designs, it is seen that the contribution to either test statistic from a pair of samples is related to the product of the sample sizes multiplied by the square of the relevant pairwise difference. Supplementary materials for this article are available online.
{"title":"Alternate Forms of the One-Way ANOVA F and Kruskal–Wallis Test Statistics","authors":"Roger W. Johnson","doi":"10.1080/26939169.2021.2025177","DOIUrl":"https://doi.org/10.1080/26939169.2021.2025177","url":null,"abstract":"Abstract For ease of instruction in the classroom, the one-way analysis of variance F statistic is rewritten in terms of pairwise differences in individual sample means instead of differences of individual sample means from the overall sample mean. Likewise, the Kruskal–Wallis statistic may be rewritten in terms of pairwise differences in individual average ranks rather than differences of individual average ranks from the overall average rank. In unbalanced designs, it is seen that the contribution to either test statistic from a pair of samples is related to the product of the sample sizes multiplied by the square of the relevant pairwise difference. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"82 - 85"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42851752","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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