Pub Date : 2022-07-13DOI: 10.1080/26939169.2022.2099487
Luna L. Sánchez Reyes, E. J. McTavish
ABSTRACT Research reproducibility is essential for scientific development. Yet, rates of reproducibility are low. As increasingly more research relies on computers and software, efforts for improving reproducibility rates have focused on making research products digitally available, such as publishing analysis workflows as computer code, and raw and processed data in computer readable form. However, research products that are digitally available are not necessarily friendly for learners and interested parties with little to no experience in the field. This renders research products unapproachable, counteracts their availability, and hinders scientific reproducibility. To improve both short- and long-term adoption of reproducible scientific practices, research products need to be made approachable for learners, the researchers of the future. Using a case study within evolutionary biology, we identify aspects of research workflows that make them unapproachable to the general audience: use of highly specialized language; unclear goals and high cognitive load; and lack of trouble-shooting examples. We propose principles to improve the unapproachable aspects of research workflows and illustrate their application using an online teaching resource. We elaborate on the general application of these principles for documenting research products and teaching materials, to provide present learners and future researchers with tools for successful scientific reproducibility. Supplementary materials for this article are available online.
{"title":"Approachable Case Studies Support Learning and Reproducibility in Data Science: An Example from Evolutionary Biology","authors":"Luna L. Sánchez Reyes, E. J. McTavish","doi":"10.1080/26939169.2022.2099487","DOIUrl":"https://doi.org/10.1080/26939169.2022.2099487","url":null,"abstract":"ABSTRACT Research reproducibility is essential for scientific development. Yet, rates of reproducibility are low. As increasingly more research relies on computers and software, efforts for improving reproducibility rates have focused on making research products digitally available, such as publishing analysis workflows as computer code, and raw and processed data in computer readable form. However, research products that are digitally available are not necessarily friendly for learners and interested parties with little to no experience in the field. This renders research products unapproachable, counteracts their availability, and hinders scientific reproducibility. To improve both short- and long-term adoption of reproducible scientific practices, research products need to be made approachable for learners, the researchers of the future. Using a case study within evolutionary biology, we identify aspects of research workflows that make them unapproachable to the general audience: use of highly specialized language; unclear goals and high cognitive load; and lack of trouble-shooting examples. We propose principles to improve the unapproachable aspects of research workflows and illustrate their application using an online teaching resource. We elaborate on the general application of these principles for documenting research products and teaching materials, to provide present learners and future researchers with tools for successful scientific reproducibility. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"304 - 310"},"PeriodicalIF":1.7,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46036461","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 : 2022-07-11DOI: 10.1080/26939169.2022.2099488
Kady Schneiter, KimberLeigh Felix Hadfield, Jenny Lee Clements
Abstract Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students’ names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size leads to isolation. At Utah State University, we offer introductory statistics classes for various audiences using a large lecture format. The authors have collectively led these large lectures dozens of times and found that, despite its shortcomings, the large lecture format can be an asset when teaching statistics. With an active learning approach such as recommended by the revised GAISE College report, large class sizes permit realistic sampling, facilitate student-driven simulation, and provide bountiful opportunities for experimentation. In this article, we discuss the benefits of large classes for statistics teaching and present examples of using large class sizes to create an engaging environment that where students are involved in active learning and collecting real data to foster statistical thinking.
{"title":"Leveraging the “Large” in Large Lecture Statistics Classes","authors":"Kady Schneiter, KimberLeigh Felix Hadfield, Jenny Lee Clements","doi":"10.1080/26939169.2022.2099488","DOIUrl":"https://doi.org/10.1080/26939169.2022.2099488","url":null,"abstract":"Abstract Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students’ names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size leads to isolation. At Utah State University, we offer introductory statistics classes for various audiences using a large lecture format. The authors have collectively led these large lectures dozens of times and found that, despite its shortcomings, the large lecture format can be an asset when teaching statistics. With an active learning approach such as recommended by the revised GAISE College report, large class sizes permit realistic sampling, facilitate student-driven simulation, and provide bountiful opportunities for experimentation. In this article, we discuss the benefits of large classes for statistics teaching and present examples of using large class sizes to create an engaging environment that where students are involved in active learning and collecting real data to foster statistical thinking.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"173 - 178"},"PeriodicalIF":1.7,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41708670","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 : 2022-07-08DOI: 10.1080/26939169.2022.2099489
Richard Ball, Norm Medeiros, Nicholas W. Bussberg, A. Piekut
ABSTRACT This article synthesizes ideas that emerged over the course of a 10-week symposium titled “Teaching Reproducible Research: Educational Outcomes” https://www.projecttier.org/fellowships-and-workshops/2021-spring-symposium that took place in the spring of 2021. The speakers included one linguist, three political scientists, seven psychologists, and three statisticians; about half of them were based in the United States and about half in the United Kingdom. The symposium focused on a particular form of reproducibility—namely computational reproducibility—and the paper begins with an exposition of what computational reproducibility is and how it can be achieved. Drawing on talks by the speakers and comments from participants, the paper then enumerates several reasons for which learning reproducible research methods enhance the education of college and university students; the benefits have partly to do with developing computational skills that prepare students for future education and employment, but they also have to do with their intellectual development more broadly. The article also distills insights from the symposium about practical strategies instructors can adopt to integrate reproducibility into their teaching, as well as to promote the practice among colleagues and throughout departmental curricula. The conceptual framework about the meaning and purposes of teaching reproducibility, and the practical guidance about how to get started, add up to an invitation to instructors to explore the potential for introducing reproducibility in their classes and research supervision.
{"title":"An Invitation to Teaching Reproducible Research: Lessons from a Symposium","authors":"Richard Ball, Norm Medeiros, Nicholas W. Bussberg, A. Piekut","doi":"10.1080/26939169.2022.2099489","DOIUrl":"https://doi.org/10.1080/26939169.2022.2099489","url":null,"abstract":"ABSTRACT This article synthesizes ideas that emerged over the course of a 10-week symposium titled “Teaching Reproducible Research: Educational Outcomes” https://www.projecttier.org/fellowships-and-workshops/2021-spring-symposium that took place in the spring of 2021. The speakers included one linguist, three political scientists, seven psychologists, and three statisticians; about half of them were based in the United States and about half in the United Kingdom. The symposium focused on a particular form of reproducibility—namely computational reproducibility—and the paper begins with an exposition of what computational reproducibility is and how it can be achieved. Drawing on talks by the speakers and comments from participants, the paper then enumerates several reasons for which learning reproducible research methods enhance the education of college and university students; the benefits have partly to do with developing computational skills that prepare students for future education and employment, but they also have to do with their intellectual development more broadly. The article also distills insights from the symposium about practical strategies instructors can adopt to integrate reproducibility into their teaching, as well as to promote the practice among colleagues and throughout departmental curricula. The conceptual framework about the meaning and purposes of teaching reproducibility, and the practical guidance about how to get started, add up to an invitation to instructors to explore the potential for introducing reproducibility in their classes and research supervision.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"209 - 218"},"PeriodicalIF":1.7,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43619158","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 : 2022-07-07DOI: 10.1080/26939169.2022.2099486
Elizabeth G. Arnold, J. Green
ABSTRACT In K–12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. We identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. For this study, we qualitatively investigated how two teachers’ instructional actions compared to what was prescribed in the annotated lesson notes. We found ways in which the teachers’ instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. From these results, we highlight affordances and limitations of annotated lesson notes for statistics instruction and offer recommendations for those who create statistics curricula with annotated lesson notes.
{"title":"Exploring the Use of Statistics Curricula with Annotated Lesson Notes","authors":"Elizabeth G. Arnold, J. Green","doi":"10.1080/26939169.2022.2099486","DOIUrl":"https://doi.org/10.1080/26939169.2022.2099486","url":null,"abstract":"ABSTRACT In K–12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. We identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. For this study, we qualitatively investigated how two teachers’ instructional actions compared to what was prescribed in the annotated lesson notes. We found ways in which the teachers’ instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. From these results, we highlight affordances and limitations of annotated lesson notes for statistics instruction and offer recommendations for those who create statistics curricula with annotated lesson notes.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"162 - 172"},"PeriodicalIF":1.7,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46329523","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 : 2022-06-23DOI: 10.1080/26939169.2022.2082602
S. Stoudt
Abstract To paraphrase John Tukey, the beauty of working with data is that you get to “play in everyone’s backyard.” A corollary to this statement is that working with data necessitates collaboration. Although students often learn technical workflows to wrangle and analyze data, these workflows may break down or require adjustment to accommodate the different stages of the writing process when it is time to face the communication phase of the project. In this article, I propose two writing workflows for use by students in a final-project setting. One workflow involves version control and aims to minimize the chance of a merge conflict throughout the writing process, and the other aims to add some level of reproducibility to a Google-Doc-heavy writing workflow (i.e., avoid manual copying and pasting). Both rely on a division of the labor, require a plan (and structure) to be created and followed by members of a team, and involve communication outside of the final report document itself. This article does not aim to solve all collaborative writing pain points but instead aims to start the conversation on how to explicitly teach students not only how to code collaboratively but to write collaboratively.
{"title":"Collaborative Writing Workflows in the Data-Driven Classroom: A Conversation Starter","authors":"S. Stoudt","doi":"10.1080/26939169.2022.2082602","DOIUrl":"https://doi.org/10.1080/26939169.2022.2082602","url":null,"abstract":"Abstract To paraphrase John Tukey, the beauty of working with data is that you get to “play in everyone’s backyard.” A corollary to this statement is that working with data necessitates collaboration. Although students often learn technical workflows to wrangle and analyze data, these workflows may break down or require adjustment to accommodate the different stages of the writing process when it is time to face the communication phase of the project. In this article, I propose two writing workflows for use by students in a final-project setting. One workflow involves version control and aims to minimize the chance of a merge conflict throughout the writing process, and the other aims to add some level of reproducibility to a Google-Doc-heavy writing workflow (i.e., avoid manual copying and pasting). Both rely on a division of the labor, require a plan (and structure) to be created and followed by members of a team, and involve communication outside of the final report document itself. This article does not aim to solve all collaborative writing pain points but instead aims to start the conversation on how to explicitly teach students not only how to code collaboratively but to write collaboratively.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"282 - 288"},"PeriodicalIF":1.7,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42875052","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 : 2022-06-16DOI: 10.1080/26939169.2022.2090467
Shuan Liao
Abstract SCRATCH, developed by the Media Lab at MIT, is a kid-friendly visual programming language, designed to introduce programming to children and teens in a “more thinkable, more meaningful, and more social” way. Although it was initially intended for K-12 students, educators have used it for higher education as well, and found it particularly helpful for those who haven’t had the privilege of learning coding before college. In this article, we propose using SCRATCH to create an interactive and fun project for introduction as a gateway to learn R in introductory or intermediate statistics courses. We begin with a literature review on recent K-12 computing education, as well as how visual coding has been used in college classrooms as an aid for teaching syntax-based coding. Then, we explain the design of the proposed project and share the observations from a pilot study in a liberal arts college with 39 students who had diverse coding experiences. We find that the most disadvantaged students are not those with no coding experience, but those with poor prior coding experience or with low coding self-efficacy. This innovative SCRATCH-to-R approach also offers us a pathway toward an inclusive pedagogy in teaching coding.
{"title":"SCRATCH to R: Toward an Inclusive Pedagogy in Teaching Coding","authors":"Shuan Liao","doi":"10.1080/26939169.2022.2090467","DOIUrl":"https://doi.org/10.1080/26939169.2022.2090467","url":null,"abstract":"Abstract SCRATCH, developed by the Media Lab at MIT, is a kid-friendly visual programming language, designed to introduce programming to children and teens in a “more thinkable, more meaningful, and more social” way. Although it was initially intended for K-12 students, educators have used it for higher education as well, and found it particularly helpful for those who haven’t had the privilege of learning coding before college. In this article, we propose using SCRATCH to create an interactive and fun project for introduction as a gateway to learn R in introductory or intermediate statistics courses. We begin with a literature review on recent K-12 computing education, as well as how visual coding has been used in college classrooms as an aid for teaching syntax-based coding. Then, we explain the design of the proposed project and share the observations from a pilot study in a liberal arts college with 39 students who had diverse coding experiences. We find that the most disadvantaged students are not those with no coding experience, but those with poor prior coding experience or with low coding self-efficacy. This innovative SCRATCH-to-R approach also offers us a pathway toward an inclusive pedagogy in teaching coding.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"45 - 56"},"PeriodicalIF":1.7,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49088732","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 : 2022-06-16DOI: 10.1080/26939169.2022.2089411
Margarita Boenig-Liptsin, A. Tanweer, A. Edmundson
Abstract This article presents the Data Science Ethos Lifecycle, a tool for engaging responsible workflow developed by an interdisciplinary team of social scientists and data scientists working with the Academic Data Science Alliance. The tool uses a data science lifecycle framework to engage data science students and practitioners with the ethical dimensions of their practice. The lifecycle supports practitioners to increase awareness of how their practice shapes and is shaped by the social world and to articulate their responsibility to public stakeholders. We discuss the theoretical foundations from the fields of Science, Technology and Society, feminist theory, and critical race theory that animate the Ethos Lifecycle and show how these orient the tool toward a normative commitment to justice and what we call the “world-making” view of data science. We introduce four conceptual lenses—positionality, power, sociotechnical systems, and narratives—that are at work in the Ethos Lifecycle and show how they can bring to light ethical and human issues in a real-world data science project.
{"title":"Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice","authors":"Margarita Boenig-Liptsin, A. Tanweer, A. Edmundson","doi":"10.1080/26939169.2022.2089411","DOIUrl":"https://doi.org/10.1080/26939169.2022.2089411","url":null,"abstract":"Abstract This article presents the Data Science Ethos Lifecycle, a tool for engaging responsible workflow developed by an interdisciplinary team of social scientists and data scientists working with the Academic Data Science Alliance. The tool uses a data science lifecycle framework to engage data science students and practitioners with the ethical dimensions of their practice. The lifecycle supports practitioners to increase awareness of how their practice shapes and is shaped by the social world and to articulate their responsibility to public stakeholders. We discuss the theoretical foundations from the fields of Science, Technology and Society, feminist theory, and critical race theory that animate the Ethos Lifecycle and show how these orient the tool toward a normative commitment to justice and what we call the “world-making” view of data science. We introduce four conceptual lenses—positionality, power, sociotechnical systems, and narratives—that are at work in the Ethos Lifecycle and show how they can bring to light ethical and human issues in a real-world data science project.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"228 - 240"},"PeriodicalIF":1.7,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49394460","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 : 2022-06-14DOI: 10.1080/26939169.2022.2089410
Mary C. Tucker, S. Shaw, Ji Yeon Son, J. Stigler
Abstract We developed an interactive online textbook that interleaves R programming activities with text as a way to facilitate students’ understanding of statistical ideas while minimizing the cognitive and emotional burden of learning programming. In this exploratory study, we characterize the attitudes and experiences of 672 undergraduate students as they used our online textbook as part of a 10-week introductory course in statistics. Students expressed negative attitudes and concerns related to R at the beginning of the course, but most developed more positive attitudes after engaging with course materials, regardless of demographic characteristics or prior programming experience. Analysis of a subgroup of students revealed that change in attitudes toward R may be linked to students’ patterns of engagement over time and students’ perceptions of the learning environment.
{"title":"Teaching Statistics and Data Analysis with R","authors":"Mary C. Tucker, S. Shaw, Ji Yeon Son, J. Stigler","doi":"10.1080/26939169.2022.2089410","DOIUrl":"https://doi.org/10.1080/26939169.2022.2089410","url":null,"abstract":"Abstract We developed an interactive online textbook that interleaves R programming activities with text as a way to facilitate students’ understanding of statistical ideas while minimizing the cognitive and emotional burden of learning programming. In this exploratory study, we characterize the attitudes and experiences of 672 undergraduate students as they used our online textbook as part of a 10-week introductory course in statistics. Students expressed negative attitudes and concerns related to R at the beginning of the course, but most developed more positive attitudes after engaging with course materials, regardless of demographic characteristics or prior programming experience. Analysis of a subgroup of students revealed that change in attitudes toward R may be linked to students’ patterns of engagement over time and students’ perceptions of the learning environment.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"18 - 32"},"PeriodicalIF":1.7,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44145114","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 : 2022-06-09DOI: 10.1080/26939169.2022.2087810
E. Burnham, E. Blankenship, Sydney E. Brown
Abstract We designed an asynchronous undergraduate introductory statistics course that focuses on simulation-based inference at the University of Nebraska-Lincoln. In this article, we describe the process we used to design the course and the structure of the course. We also discuss feedback and comments we received from students on the course evaluations, and we reflect on the course after teaching it for the past three years. Our goal is to provide useful tips and ideas for instructors who have developed or are developing their own asynchronous introductory course. While we emphasize simulation-based inference in our course, we believe that many of the design features of this course would be useful for those using a traditional approach to inference in their introductory courses. Supplementary materials for this article are available online.
{"title":"Designing a Large, Online Simulation-Based Introductory Statistics Course","authors":"E. Burnham, E. Blankenship, Sydney E. Brown","doi":"10.1080/26939169.2022.2087810","DOIUrl":"https://doi.org/10.1080/26939169.2022.2087810","url":null,"abstract":"Abstract We designed an asynchronous undergraduate introductory statistics course that focuses on simulation-based inference at the University of Nebraska-Lincoln. In this article, we describe the process we used to design the course and the structure of the course. We also discuss feedback and comments we received from students on the course evaluations, and we reflect on the course after teaching it for the past three years. Our goal is to provide useful tips and ideas for instructors who have developed or are developing their own asynchronous introductory course. While we emphasize simulation-based inference in our course, we believe that many of the design features of this course would be useful for those using a traditional approach to inference in their introductory courses. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"66 - 73"},"PeriodicalIF":1.7,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46314257","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 : 2022-05-23DOI: 10.1080/26939169.2023.2165989
Maria Tackett
Abstract As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This article describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with nonmajors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The article includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The article concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the article, with full sample assignments and resources for finding data in the supplemental materials. Supplementary materials for this article are available online.
{"title":"Three Principles for Modernizing an Undergraduate Regression Analysis Course","authors":"Maria Tackett","doi":"10.1080/26939169.2023.2165989","DOIUrl":"https://doi.org/10.1080/26939169.2023.2165989","url":null,"abstract":"Abstract As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This article describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with nonmajors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The article includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The article concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the article, with full sample assignments and resources for finding data in the supplemental materials. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"116 - 127"},"PeriodicalIF":1.7,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42635580","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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