Pub Date : 2022-01-02DOI: 10.1080/26939169.2021.2020697
Jacqueline Herman, April Kerby-Helm
ABSTRACT Many statistics education researchers have found that statistics students’ attitudes tend to decrease over the duration of a course. Although many researchers have tried to incorporate a variety of activities and/or teaching methods to improve student attitudes, many are not only very time consuming to implement, but have also not shown many favorable results. In the study presented here, the inclusion of a low-stakes and easy-to-implement assignment and its effect on student attitudes is investigated. Although the results presented here did not show large changes, they suggest that some changes in students’ attitudes can be made with a small change in a course.
{"title":"Question of the Week: Can a Low-Stakes Assignment Improve Students’ Attitudes?","authors":"Jacqueline Herman, April Kerby-Helm","doi":"10.1080/26939169.2021.2020697","DOIUrl":"https://doi.org/10.1080/26939169.2021.2020697","url":null,"abstract":"ABSTRACT Many statistics education researchers have found that statistics students’ attitudes tend to decrease over the duration of a course. Although many researchers have tried to incorporate a variety of activities and/or teaching methods to improve student attitudes, many are not only very time consuming to implement, but have also not shown many favorable results. In the study presented here, the inclusion of a low-stakes and easy-to-implement assignment and its effect on student attitudes is investigated. Although the results presented here did not show large changes, they suggest that some changes in students’ attitudes can be made with a small change in a course.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"39 - 44"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43596852","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: 10.1080/26939169.2022.2034489
JaCoya Thompson, Golnaz Arastoopour Irgens
Abstract Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science curricula focuses on the undergraduate university level. In this study, we developed an introductory data science curriculum for an out of school enrichment program aimed at middle grade learners (ages 11–13). We observed how the participants in the program (n = 11) learned data science practices through the combination of nonprogramming activities and programming activities using the language R. The results revealed that participants in the program were able to investigate statistical questions of their creation, perform data analysis using statistics and the creation of data visuals, make meaning from their results, and communicate their findings. These results suggest that a series of learner-centered nonprogramming and programming activities using R can facilitate the learning of data science skills for middle-school age students.
{"title":"Data Detectives: A Data Science Program for Middle Grade Learners","authors":"JaCoya Thompson, Golnaz Arastoopour Irgens","doi":"10.1080/26939169.2022.2034489","DOIUrl":"https://doi.org/10.1080/26939169.2022.2034489","url":null,"abstract":"Abstract Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science curricula focuses on the undergraduate university level. In this study, we developed an introductory data science curriculum for an out of school enrichment program aimed at middle grade learners (ages 11–13). We observed how the participants in the program (n = 11) learned data science practices through the combination of nonprogramming activities and programming activities using the language R. The results revealed that participants in the program were able to investigate statistical questions of their creation, perform data analysis using statistics and the creation of data visuals, make meaning from their results, and communicate their findings. These results suggest that a series of learner-centered nonprogramming and programming activities using R can facilitate the learning of data science skills for middle-school age students.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"29 - 38"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43630771","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: 10.1080/26939169.2022.2033561
Allan Rossman, F. Simpson
{"title":"Interview with Felicia Simpson: Statistics at an HBCU","authors":"Allan Rossman, F. Simpson","doi":"10.1080/26939169.2022.2033561","DOIUrl":"https://doi.org/10.1080/26939169.2022.2033561","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"75 - 81"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47302299","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: 10.1080/26939169.2021.2024777
D. Tay
Abstract Metaphors are well-known tools for teaching statistics to novices. However, educators might overlook metaphor theoretical developments that offer nuanced and testable perspectives on their pedagogical applications. This article introduces the notion of metaphor types—“correspondence” (CO) and “class inclusion” (CI)—as different strategic ways of presenting metaphors and reports an experimental study on their effectiveness in teaching basic regression to language and communication majors. Briefly, CO emphasizes systematic links while CI emphasizes holistic perceptions of similarity between the source and target of a metaphor. Both competency and attitudinal measures were compared in view of the latter’s importance as intended outcomes of the typical introductory course. The results show that while CO outperformed CI in assessments of manual calculations (e.g., SST/SSR/SSE/R2), CI outperformed CO in essay assessments requiring elaboration of general conceptual understanding. CI was also linked to more positive perceptions of the practical utility of regression analysis and its contribution to personal growth. Correlations between performance and attitudes were stronger in CO than CI, which further suggests CO’s greater perceived resemblance to a “rote learning” approach. The attendant implications are discussed in the growing context of general statistics education for nonstatistics majors. Directions for further research are suggested.
{"title":"Metaphor Types as Strategies for Teaching Regression to Novice Learners","authors":"D. Tay","doi":"10.1080/26939169.2021.2024777","DOIUrl":"https://doi.org/10.1080/26939169.2021.2024777","url":null,"abstract":"Abstract Metaphors are well-known tools for teaching statistics to novices. However, educators might overlook metaphor theoretical developments that offer nuanced and testable perspectives on their pedagogical applications. This article introduces the notion of metaphor types—“correspondence” (CO) and “class inclusion” (CI)—as different strategic ways of presenting metaphors and reports an experimental study on their effectiveness in teaching basic regression to language and communication majors. Briefly, CO emphasizes systematic links while CI emphasizes holistic perceptions of similarity between the source and target of a metaphor. Both competency and attitudinal measures were compared in view of the latter’s importance as intended outcomes of the typical introductory course. The results show that while CO outperformed CI in assessments of manual calculations (e.g., SST/SSR/SSE/R2), CI outperformed CO in essay assessments requiring elaboration of general conceptual understanding. CI was also linked to more positive perceptions of the practical utility of regression analysis and its contribution to personal growth. Correlations between performance and attitudes were stronger in CO than CI, which further suggests CO’s greater perceived resemblance to a “rote learning” approach. The attendant implications are discussed in the growing context of general statistics education for nonstatistics majors. Directions for further research are suggested.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"3 - 14"},"PeriodicalIF":1.7,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49493338","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 : 2021-10-12DOI: 10.1080/26939169.2023.2165988
M. Dogucu, Alicia M. Johnson, Miles Q. Ott
Abstract Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. Though some common strategies apply across these areas, this article focuses on providing a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from our experience co-writing a statistics textbook. In turn, this framework establishes a structure for holding ourselves accountable to these principles.
{"title":"Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses","authors":"M. Dogucu, Alicia M. Johnson, Miles Q. Ott","doi":"10.1080/26939169.2023.2165988","DOIUrl":"https://doi.org/10.1080/26939169.2023.2165988","url":null,"abstract":"Abstract Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. Though some common strategies apply across these areas, this article focuses on providing a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from our experience co-writing a statistics textbook. In turn, this framework establishes a structure for holding ourselves accountable to these principles.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"144 - 150"},"PeriodicalIF":1.7,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42715114","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 : 2021-09-17DOI: 10.1080/26939169.2022.2074922
Joel Ostblom, T. Timbers
Abstract In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modeling, which is often one of the most interesting topics to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we motivate, guide, and provide ample practice opportunities to data science students to effectively engage them in learning about this topic.
{"title":"Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice","authors":"Joel Ostblom, T. Timbers","doi":"10.1080/26939169.2022.2074922","DOIUrl":"https://doi.org/10.1080/26939169.2022.2074922","url":null,"abstract":"Abstract In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modeling, which is often one of the most interesting topics to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we motivate, guide, and provide ample practice opportunities to data science students to effectively engage them in learning about this topic.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"241 - 250"},"PeriodicalIF":1.7,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45383333","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 : 2021-09-02DOI: 10.1080/26939169.2021.1971586
R. Peng, Athena Chen, Eric W. Bridgeford, J. Leek, Stephanie C. Hicks
Abstract Teaching data analysis by providing students with real-world problems and datasets allows students to integrate a variety of skills in a situation that mirrors how data analysis actually works. However, whole data analyses may obscure the individual skills of data analytic practice that are generalizable across data analyses. One such skill is the ability to diagnose the cause of unexpected results in a data analysis. While experienced analysts can quickly iterate through a series of potential explanations when confronted with unexpected results, novice analysts often struggle to figure out how to move forward. The goal of this article is to describe an approach to teaching students skills in diagnosing data analytic problems. The exercise described here is targeted to allow students to practice this skill and to assess the depth of their knowledge about the statistical tools they have learned. We take a hypothetical case study approach and focus on the students’ reasoning through their diagnoses and suggestions for follow-up action. We found the implementation of this exercise in a small graduate course to provide valuable information about the students’ diagnostic thought processes, but further work is needed regarding structured approaches to implementation and the design of assessments. Supplementary materials for this article are available online.
{"title":"Diagnosing Data Analytic Problems in the Classroom","authors":"R. Peng, Athena Chen, Eric W. Bridgeford, J. Leek, Stephanie C. Hicks","doi":"10.1080/26939169.2021.1971586","DOIUrl":"https://doi.org/10.1080/26939169.2021.1971586","url":null,"abstract":"Abstract Teaching data analysis by providing students with real-world problems and datasets allows students to integrate a variety of skills in a situation that mirrors how data analysis actually works. However, whole data analyses may obscure the individual skills of data analytic practice that are generalizable across data analyses. One such skill is the ability to diagnose the cause of unexpected results in a data analysis. While experienced analysts can quickly iterate through a series of potential explanations when confronted with unexpected results, novice analysts often struggle to figure out how to move forward. The goal of this article is to describe an approach to teaching students skills in diagnosing data analytic problems. The exercise described here is targeted to allow students to practice this skill and to assess the depth of their knowledge about the statistical tools they have learned. We take a hypothetical case study approach and focus on the students’ reasoning through their diagnoses and suggestions for follow-up action. We found the implementation of this exercise in a small graduate course to provide valuable information about the students’ diagnostic thought processes, but further work is needed regarding structured approaches to implementation and the design of assessments. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"29 1","pages":"267 - 276"},"PeriodicalIF":1.7,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45197192","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 : 2021-09-02DOI: 10.1080/26939169.2021.1994489
W. C. Amdat
Abstract The Chicago Hardship Index is a proposed starting point for introducing students to structural urban inequities. ASA’s mission statement to use statistics to enhance human welfare serves as a motivation for social justice projects. This article contains an application of ASA’s ethical guidelines to such projects, background information about the history and landscape of Chicago community areas, and practical ideas for how to combine hardship index data with learning statistical and data science tools. Supplementary materials for this article are available online.
{"title":"The Chicago Hardship Index: An Introduction to Urban Inequity","authors":"W. C. Amdat","doi":"10.1080/26939169.2021.1994489","DOIUrl":"https://doi.org/10.1080/26939169.2021.1994489","url":null,"abstract":"Abstract The Chicago Hardship Index is a proposed starting point for introducing students to structural urban inequities. ASA’s mission statement to use statistics to enhance human welfare serves as a motivation for social justice projects. This article contains an application of ASA’s ethical guidelines to such projects, background information about the history and landscape of Chicago community areas, and practical ideas for how to combine hardship index data with learning statistical and data science tools. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"29 1","pages":"328 - 336"},"PeriodicalIF":1.7,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42834110","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 : 2021-09-02DOI: 10.1080/26939169.2021.1971587
Eric A. Vance
ABSTRACT Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.
{"title":"Using Team-Based Learning to Teach Data Science","authors":"Eric A. Vance","doi":"10.1080/26939169.2021.1971587","DOIUrl":"https://doi.org/10.1080/26939169.2021.1971587","url":null,"abstract":"ABSTRACT Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"29 1","pages":"277 - 296"},"PeriodicalIF":1.7,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44017282","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 : 2021-09-02DOI: 10.1080/26939169.2021.2013017
J. Witmer
{"title":"Note from the Editor","authors":"J. Witmer","doi":"10.1080/26939169.2021.2013017","DOIUrl":"https://doi.org/10.1080/26939169.2021.2013017","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"29 1","pages":"217 - 217"},"PeriodicalIF":1.7,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42899795","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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