Pub Date : 2022-10-28DOI: 10.1080/26939169.2022.2141155
N. Taback, Alison L Gibbs
Abstract Can a “nudge” toward engaging, fun, and useful material improve student attitudes toward statistics? We report on the results of a randomized study to assess the effect of a “nudge” delivered via a weekly E-mail digest on the attitudes of students enrolled in a large introductory statistics course taught in both flipped and fully online formats. Students were randomized to receive either a personalized weekly E-mail digest with course information and a “nudge” to read and explore interesting applications of statistics relevant to the weekly course material, or a generic course E-mail digest with the same course information, and no “nudge.” Our study found no evidence that “nudging” students to read and explore interesting applications of statistics resulted in better attitudes toward statistics. Supplementary materials for this article are available online.
{"title":"A Randomized Study to Evaluate the Effect of a Nudge via Weekly E-mails on Students’ Attitudes Toward Statistics","authors":"N. Taback, Alison L Gibbs","doi":"10.1080/26939169.2022.2141155","DOIUrl":"https://doi.org/10.1080/26939169.2022.2141155","url":null,"abstract":"Abstract Can a “nudge” toward engaging, fun, and useful material improve student attitudes toward statistics? We report on the results of a randomized study to assess the effect of a “nudge” delivered via a weekly E-mail digest on the attitudes of students enrolled in a large introductory statistics course taught in both flipped and fully online formats. Students were randomized to receive either a personalized weekly E-mail digest with course information and a “nudge” to read and explore interesting applications of statistics relevant to the weekly course material, or a generic course E-mail digest with the same course information, and no “nudge.” Our study found no evidence that “nudging” students to read and explore interesting applications of statistics resulted in better attitudes toward statistics. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44529487","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 : 2022-10-26DOI: 10.1080/26939169.2022.2138801
Davit Khachatryan
Abstract According to decades of research in educational psychology, learning is a social process that is enhanced when it happens in contexts that are familiar and relevant. But because of the skyrocketing popularity of data science, today we often work with students coming from an abundance of academic concentrations, professional, and personal backgrounds. How can our teaching account for the existing multiplicity of interests and be inclusive of diverse cultural, socioeconomic, and professional backgrounds? Music is a convenient medium that can engage and include. Enter Playmeans, a novel web application (“app”) that enables students to perform unsupervised learning while exploring music. The flexible user interface lets a student select their favorite artist and acquire, in real time, the corresponding discography in a matter of seconds. The student then interacts with the acquired data by means of visualizing, clustering, and, most importantly, listening to music—all of which are happening within the novel Playmeans app. Supplementary materials for this article are available online.
{"title":"Playmeans: Inclusive and Engaging Data Science through Music","authors":"Davit Khachatryan","doi":"10.1080/26939169.2022.2138801","DOIUrl":"https://doi.org/10.1080/26939169.2022.2138801","url":null,"abstract":"Abstract According to decades of research in educational psychology, learning is a social process that is enhanced when it happens in contexts that are familiar and relevant. But because of the skyrocketing popularity of data science, today we often work with students coming from an abundance of academic concentrations, professional, and personal backgrounds. How can our teaching account for the existing multiplicity of interests and be inclusive of diverse cultural, socioeconomic, and professional backgrounds? Music is a convenient medium that can engage and include. Enter Playmeans, a novel web application (“app”) that enables students to perform unsupervised learning while exploring music. The flexible user interface lets a student select their favorite artist and acquire, in real time, the corresponding discography in a matter of seconds. The student then interacts with the acquired data by means of visualizing, clustering, and, most importantly, listening to music—all of which are happening within the novel Playmeans app. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46220174","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 : 2022-10-26DOI: 10.1080/26939169.2022.2138800
Laura A. Hildreth, Michelle Miley, Erin Strickland, Jacob Swisher
Abstract Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics graduate students. We also present results of a survey administered prior to the first workshop and after the last workshop to assess changes in attitudes towards writing and obtain student feedback to improve the workshops. Lastly, we discuss potential modifications of these workshops including ways to use these workshops or elements from them for undergraduates or in statistics and data science courses. Supplementary materials for this article are available online.
{"title":"Writing Workshops to Foster Written Communication Skills in Statistics Graduate Students","authors":"Laura A. Hildreth, Michelle Miley, Erin Strickland, Jacob Swisher","doi":"10.1080/26939169.2022.2138800","DOIUrl":"https://doi.org/10.1080/26939169.2022.2138800","url":null,"abstract":"Abstract Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics graduate students. We also present results of a survey administered prior to the first workshop and after the last workshop to assess changes in attitudes towards writing and obtain student feedback to improve the workshops. Lastly, we discuss potential modifications of these workshops including ways to use these workshops or elements from them for undergraduates or in statistics and data science courses. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44713950","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 : 2022-10-04DOI: 10.1080/26939169.2022.2132325
Claudia C. Sutter, Karen B. Givvin, Mary C. Tucker, Kathryn A. Givvin, Ana Leandro-Ramos, Paige L. Solomon
ABSTRACT The COVID-19 pandemic and shift to remote instruction disrupted students’ learning and well-being. This study explored undergraduates’ incoming course concerns and later perceived challenges in an introductory statistics course. We explored how the frequency of concerns changed with the onset of COVID-19 ( 1417) and, during COVID-19, how incoming concerns compared to later perceived challenges ( 524). Students were most concerned about R coding, understanding concepts, workload, prior knowledge, time management, and performance, with each of these concerns mentioned less frequently during than before COVID-19. Concerns most directly related to the pandemic—virtual learning and inaccessibility of resources—showed an increase in frequency. The frequency of concerns differed by gender and URM status. The most frequently mentioned challenges were course workload, virtual learning, R coding, and understanding concepts, with significant differences by URM status. Concerns about understanding concepts, lack of prior knowledge, performance, and time management declined from the beginning to the end of the term. Workload had the highest rate of both consistency and emergence across the term. Because students’ perceptions have an impact on their experiences and expectations, understanding and addressing concerns and challenges could help guide instructional designers and policymakers as they develop interventions.
{"title":"Student Concerns and Perceived Challenges in Introductory Statistics, How the Frequency Shifted during COVID-19, and How They Differ by Subgroups of Students","authors":"Claudia C. Sutter, Karen B. Givvin, Mary C. Tucker, Kathryn A. Givvin, Ana Leandro-Ramos, Paige L. Solomon","doi":"10.1080/26939169.2022.2132325","DOIUrl":"https://doi.org/10.1080/26939169.2022.2132325","url":null,"abstract":"ABSTRACT The COVID-19 pandemic and shift to remote instruction disrupted students’ learning and well-being. This study explored undergraduates’ incoming course concerns and later perceived challenges in an introductory statistics course. We explored how the frequency of concerns changed with the onset of COVID-19 ( 1417) and, during COVID-19, how incoming concerns compared to later perceived challenges ( 524). Students were most concerned about R coding, understanding concepts, workload, prior knowledge, time management, and performance, with each of these concerns mentioned less frequently during than before COVID-19. Concerns most directly related to the pandemic—virtual learning and inaccessibility of resources—showed an increase in frequency. The frequency of concerns differed by gender and URM status. The most frequently mentioned challenges were course workload, virtual learning, R coding, and understanding concepts, with significant differences by URM status. Concerns about understanding concepts, lack of prior knowledge, performance, and time management declined from the beginning to the end of the term. Workload had the highest rate of both consistency and emergence across the term. Because students’ perceptions have an impact on their experiences and expectations, understanding and addressing concerns and challenges could help guide instructional designers and policymakers as they develop interventions.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44588958","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 : 2022-09-27DOI: 10.1080/26939169.2022.2128118
Yonggang Lu, Qiujie Zheng, Daniel Quinn
Abstract We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal inference with an emphasis on probabilistic reasoning and causal assumption. It also reveals the relevance and distinction between causal and statistical inference. Using a freeware tool, we demonstrate our approach with five examples that instructors can use to introduce students at different levels to the conception of causality, motivate them to learn more concepts for causal inference, and demonstrate practical applications of causal inference. We also provide detailed suggestions on using the five examples in the classroom.
{"title":"Introducing Causal Inference Using Bayesian Networks and do-Calculus","authors":"Yonggang Lu, Qiujie Zheng, Daniel Quinn","doi":"10.1080/26939169.2022.2128118","DOIUrl":"https://doi.org/10.1080/26939169.2022.2128118","url":null,"abstract":"Abstract We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal inference with an emphasis on probabilistic reasoning and causal assumption. It also reveals the relevance and distinction between causal and statistical inference. Using a freeware tool, we demonstrate our approach with five examples that instructors can use to introduce students at different levels to the conception of causality, motivate them to learn more concepts for causal inference, and demonstrate practical applications of causal inference. We also provide detailed suggestions on using the five examples in the classroom.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45105937","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 : 2022-09-27DOI: 10.1080/26939169.2022.2128119
Wen Huang, J. London, Logan A. Perry
Abstract Understanding statistics is essential for engineers. However, statistics courses remain challenging for many students, as they find them rigid, abstract, and demanding. Prior research has indicated that using project-based learning (PjBL) to demonstrate the relevance of statistics to students can have a significant effect on learning in these courses. Consequently, this study sought to explore the impact of a PjBL intervention on student perceptions of the relevance of engineering and statistics. The purpose of the intervention was to help students understand the connection between statistics and their academic majors, lives, and future careers. Four mini-projects connecting statistics to students’ experiences and future careers were designed and implemented during a 16-week course and students’ perceptions were compared to those of students who took a traditional statistics course. Students enrolled in the experimental group (a synchronous learning experience) and the control group (an online learning experience) were sent the same survey at the end of the semester. The survey results suggest that the PjBL intervention could potentially increase students’ understanding of the usefulness of statistics and effectively enhance their perceptions of belonging to the engineering community. This study summarizes the results of this PjBL intervention, the limitations of the research design, and suggests implications for improving future statistics courses in the context of engineering.
{"title":"Project-Based Learning Promotes Students’ Perceived Relevance in an Engineering Statistics Course: A Comparison of Learning in Synchronous and Online Learning Environments","authors":"Wen Huang, J. London, Logan A. Perry","doi":"10.1080/26939169.2022.2128119","DOIUrl":"https://doi.org/10.1080/26939169.2022.2128119","url":null,"abstract":"Abstract Understanding statistics is essential for engineers. However, statistics courses remain challenging for many students, as they find them rigid, abstract, and demanding. Prior research has indicated that using project-based learning (PjBL) to demonstrate the relevance of statistics to students can have a significant effect on learning in these courses. Consequently, this study sought to explore the impact of a PjBL intervention on student perceptions of the relevance of engineering and statistics. The purpose of the intervention was to help students understand the connection between statistics and their academic majors, lives, and future careers. Four mini-projects connecting statistics to students’ experiences and future careers were designed and implemented during a 16-week course and students’ perceptions were compared to those of students who took a traditional statistics course. Students enrolled in the experimental group (a synchronous learning experience) and the control group (an online learning experience) were sent the same survey at the end of the semester. The survey results suggest that the PjBL intervention could potentially increase students’ understanding of the usefulness of statistics and effectively enhance their perceptions of belonging to the engineering community. This study summarizes the results of this PjBL intervention, the limitations of the research design, and suggests implications for improving future statistics courses in the context of engineering.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42338541","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 : 2022-09-02DOI: 10.1080/26939169.2022.2141001
N. Horton, Rohan Alexander, M. Parker, A. Piekut, Colin W. Rundel
Modern statistics and data science uses an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner. Models such as the PPDAC (Problem, Plan, Data, Analysis, Conclusion) Cycle (n.d) have been widely adopted in many secondary and post-secondary classrooms (see the review by Lee et al. 2022). The importance of the data analysis cycle has also been described and reinforced in guidelines for statistics majors (ASA Curriculum Guidelines 2014), undergraduate data science curricula (ACM 2021), and in data science courses and teaching materials (e.g., Wickham and Grolemund 2022). In 2018, the National Academies of Science, Engineering, and Medicine’s “Data Science for Undergraduates” consensus study (NASEM 2018) broadened the definition of the data analysis cycle by identifying the importance of workflow and reproducibility as a component of data acumen needed in our graduates. The report noted that “documenting, incrementally improving, sharing, and generalizing such workflows are an important part of data science practice owing to the team nature of data science and broader significance of scientific reproducibility and replicability.” The report also tied issues of reproducibility and workflow to the ethical conduct of science. The importance of others being able to have confidence in our findings is built into the foundations of statistics and data science (Parashar, Heroux, and Stodden 2022). For instance, in theoretical research, theorems are introduced along with their proof. As statistics has changed to rely more on computational methods, innovation is needed to ensure that the same level of rigor characterizes claims based on data and code. Efforts to foster reproducibility in science (NASEM 2019; Parashar, Heroux, and Stodden 2022) and to accelerate scientific discoveries (NASEM 2021) have highlighted the importance of reproducibility and workflow within the broader scientific process. Robust workflows matter. For instance, COVID-19 counts in the United Kingdom were underestimated because the way that Excel was used resulted in dropped data (Kelion 2020). The economists Carmen Reinhart and Kenneth Rogoff made Excel errors that resulted in miscalculated GDP growth rates (Herndon, Ash, and Pollin 2014). Cut and paste errors are all too common in many workflows (Perkel 2022). The reproducibility crisis that was first identified in psychology is now known to afflict much of the physical and social sciences. Steps taken to address this crisis, including improved reporting of methods, code and data sharing, version control, are increasingly com-
现代统计学和数据科学使用迭代的数据分析过程来解决问题，并以可重复的方式从数据中提取意义。诸如PPDAC(问题、计划、数据、分析、结论)周期(n.d)等模型已在许多中学和高等学校的课堂上广泛采用(参见Lee et al. 2022的评论)。数据分析周期的重要性也在统计学专业指南(2014年ASA课程指南)、本科数据科学课程(ACM 2021)以及数据科学课程和教材(例如，Wickham和Grolemund 2022)中得到了描述和加强。2018年，美国国家科学院、工程院和医学院的“本科生数据科学”共识研究(NASEM 2018)通过确定工作流程和可重复性作为毕业生所需数据敏锐度组成部分的重要性，拓宽了数据分析周期的定义。该报告指出，“由于数据科学的团队性质以及科学可重复性和可复制性的更广泛意义，记录、逐步改进、共享和推广这些工作流程是数据科学实践的重要组成部分。”该报告还将可重复性和工作流程问题与科学的道德行为联系起来。其他人能够对我们的发现有信心的重要性是建立在统计学和数据科学的基础上的(Parashar, Heroux, and Stodden 2022)。例如，在理论研究中，定理和它们的证明一起被引入。由于统计已经变得更加依赖计算方法，因此需要创新，以确保基于数据和代码的索赔具有相同的严谨性。努力促进科学的可重复性(NASEM 2019;Parashar, Heroux, and Stodden(2022)和加速科学发现(NASEM 2021)强调了可重复性和工作流程在更广泛的科学过程中的重要性。健壮的工作流很重要。例如，英国的COVID-19计数被低估了，因为使用Excel的方式导致数据丢失(Kelion 2020)。经济学家卡门·莱因哈特(Carmen Reinhart)和肯尼斯·罗格夫(Kenneth Rogoff)在Excel中犯了错误，导致误算了GDP增长率(Herndon, Ash, and Pollin 2014)。剪切和粘贴错误在许多工作流中都很常见(Perkel 2022)。最初在心理学中发现的可再现性危机，现在被认为在物理科学和社会科学的大部分领域都受到了影响。为解决这一危机而采取的措施，包括改进报告方法、代码和数据共享、版本控制，正在日益普及
{"title":"The Growing Importance of Reproducibility and Responsible Workflow in the Data Science and Statistics Curriculum","authors":"N. Horton, Rohan Alexander, M. Parker, A. Piekut, Colin W. Rundel","doi":"10.1080/26939169.2022.2141001","DOIUrl":"https://doi.org/10.1080/26939169.2022.2141001","url":null,"abstract":"Modern statistics and data science uses an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner. Models such as the PPDAC (Problem, Plan, Data, Analysis, Conclusion) Cycle (n.d) have been widely adopted in many secondary and post-secondary classrooms (see the review by Lee et al. 2022). The importance of the data analysis cycle has also been described and reinforced in guidelines for statistics majors (ASA Curriculum Guidelines 2014), undergraduate data science curricula (ACM 2021), and in data science courses and teaching materials (e.g., Wickham and Grolemund 2022). In 2018, the National Academies of Science, Engineering, and Medicine’s “Data Science for Undergraduates” consensus study (NASEM 2018) broadened the definition of the data analysis cycle by identifying the importance of workflow and reproducibility as a component of data acumen needed in our graduates. The report noted that “documenting, incrementally improving, sharing, and generalizing such workflows are an important part of data science practice owing to the team nature of data science and broader significance of scientific reproducibility and replicability.” The report also tied issues of reproducibility and workflow to the ethical conduct of science. The importance of others being able to have confidence in our findings is built into the foundations of statistics and data science (Parashar, Heroux, and Stodden 2022). For instance, in theoretical research, theorems are introduced along with their proof. As statistics has changed to rely more on computational methods, innovation is needed to ensure that the same level of rigor characterizes claims based on data and code. Efforts to foster reproducibility in science (NASEM 2019; Parashar, Heroux, and Stodden 2022) and to accelerate scientific discoveries (NASEM 2021) have highlighted the importance of reproducibility and workflow within the broader scientific process. Robust workflows matter. For instance, COVID-19 counts in the United Kingdom were underestimated because the way that Excel was used resulted in dropped data (Kelion 2020). The economists Carmen Reinhart and Kenneth Rogoff made Excel errors that resulted in miscalculated GDP growth rates (Herndon, Ash, and Pollin 2014). Cut and paste errors are all too common in many workflows (Perkel 2022). The reproducibility crisis that was first identified in psychology is now known to afflict much of the physical and social sciences. Steps taken to address this crisis, including improved reporting of methods, code and data sharing, version control, are increasingly com-","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45202262","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 : 2022-08-30DOI: 10.1080/26939169.2022.2118644
C. Mehta, Renee’ H. Moore
Abstract Collaborative statisticians are instrumental to maintaining the rigor and reproducibility of multidisciplinary projects on which they collaborate. Clear, complete, well-annotated records of data, its revising, analysis, interpretation, and presentation are essential. Many students do not know how to systematically and consistently organize their digital files and cannot reliably replicate (or find!) work that may have been performed on a collaborative project. This article describes a newly developed and required one semester collaborative statistics course that includes the explicit teaching of project organization in lecture, emphasis of these skills on linked homework assignments, and their closely supervised practice through a mentored collaborative project with multidisciplinary researchers. The tripartite design provided exposure to project organization concepts during the course, encouraged straightforward implementation through homework assignments, and then challenged students with a real-world experience during a collaborative project. Supplementary materials for this article are available online.
{"title":"Third Time’s a Charm: A Tripartite Approach for Teaching Project Organization to Students","authors":"C. Mehta, Renee’ H. Moore","doi":"10.1080/26939169.2022.2118644","DOIUrl":"https://doi.org/10.1080/26939169.2022.2118644","url":null,"abstract":"Abstract Collaborative statisticians are instrumental to maintaining the rigor and reproducibility of multidisciplinary projects on which they collaborate. Clear, complete, well-annotated records of data, its revising, analysis, interpretation, and presentation are essential. Many students do not know how to systematically and consistently organize their digital files and cannot reliably replicate (or find!) work that may have been performed on a collaborative project. This article describes a newly developed and required one semester collaborative statistics course that includes the explicit teaching of project organization in lecture, emphasis of these skills on linked homework assignments, and their closely supervised practice through a mentored collaborative project with multidisciplinary researchers. The tripartite design provided exposure to project organization concepts during the course, encouraged straightforward implementation through homework assignments, and then challenged students with a real-world experience during a collaborative project. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44842166","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 : 2022-08-30DOI: 10.1080/26939169.2022.2118645
J. Towse, R. Davies, Ellie Ball, Rebecca James, Ben Gooding, Matthew Ivory
Abstract We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the Lancaster University STatistics REsources (LUSTRE) package, used to manage student research project data in psychology and build capacity with respect to data acumen. LUSTRE provides a safe space to engage students with open research practices—by making tangible different phases of the reproducible research pipeline, while emphasizing its value as a transferable skill. It is an open-source online data catalogue that captures key data management information about a student research project of potential relevance to data scientists. Embedded within a taught programme, it also highlights concepts and examples of data management processes. We document a portfolio of open teaching resources for LUSTRE, and consider how others can implement or adapt them to facilitate data management and open research. We discuss the role of LUSTRE as a; (a) resource and set of activities for promoting good data management practices; (b) framework to enable the delivery of key concepts in open research; (c) an online system to organize and showcase project work.
{"title":"LUSTRE: An Online Data Management and Student Project Resource","authors":"J. Towse, R. Davies, Ellie Ball, Rebecca James, Ben Gooding, Matthew Ivory","doi":"10.1080/26939169.2022.2118645","DOIUrl":"https://doi.org/10.1080/26939169.2022.2118645","url":null,"abstract":"Abstract We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the Lancaster University STatistics REsources (LUSTRE) package, used to manage student research project data in psychology and build capacity with respect to data acumen. LUSTRE provides a safe space to engage students with open research practices—by making tangible different phases of the reproducible research pipeline, while emphasizing its value as a transferable skill. It is an open-source online data catalogue that captures key data management information about a student research project of potential relevance to data scientists. Embedded within a taught programme, it also highlights concepts and examples of data management processes. We document a portfolio of open teaching resources for LUSTRE, and consider how others can implement or adapt them to facilitate data management and open research. We discuss the role of LUSTRE as a; (a) resource and set of activities for promoting good data management practices; (b) framework to enable the delivery of key concepts in open research; (c) an online system to organize and showcase project work.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42960879","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 : 2022-08-09DOI: 10.1080/26939169.2022.2111008
J. O. Holman, Allie Hacherl
Abstract It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly applicable statistical computing methods. This article describes efforts to teach Monte Carlo simulation using Python. A series of simulation assignments are completed first in Google Sheets, as described in a previous article. Then, the same simulation assignments are completed in Python, as detailed in this article. This pedagogical strategy appears to support student learning for those who are unfamiliar with statistical computing but familiar with the use of spreadsheets. Supplementary materials for this article are available online.
{"title":"Teaching Monte Carlo Simulation with Python","authors":"J. O. Holman, Allie Hacherl","doi":"10.1080/26939169.2022.2111008","DOIUrl":"https://doi.org/10.1080/26939169.2022.2111008","url":null,"abstract":"Abstract It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly applicable statistical computing methods. This article describes efforts to teach Monte Carlo simulation using Python. A series of simulation assignments are completed first in Google Sheets, as described in a previous article. Then, the same simulation assignments are completed in Python, as detailed in this article. This pedagogical strategy appears to support student learning for those who are unfamiliar with statistical computing but familiar with the use of spreadsheets. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45881587","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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