Pub Date : 2020-01-02DOI: 10.1080/10691898.2020.1733342
Allan Rossman, L. Lesser
Larry Lesser is a Professor in the Department of Mathematical Sciences at The University of Texas at El Paso. He is also a UTEP Distinguished Teaching Professor whose awards include a 2016 Minnie Stevens Piper Professor Award, the 2012 International Sun Conference Scholarship of Teaching and Learning Award, a 2011 UT System Regents’ Outstanding Teaching Award, and the MAA Southwestern Section’s 2010 Distinguished Teaching Award.This interview took place via email from January 17–February 16, 2020. Photo courtesy of Lauren Davis.
{"title":"Interview With Larry Lesser","authors":"Allan Rossman, L. Lesser","doi":"10.1080/10691898.2020.1733342","DOIUrl":"https://doi.org/10.1080/10691898.2020.1733342","url":null,"abstract":"Larry Lesser is a Professor in the Department of Mathematical Sciences at The University of Texas at El Paso. He is also a UTEP Distinguished Teaching Professor whose awards include a 2016 Minnie Stevens Piper Professor Award, the 2012 International Sun Conference Scholarship of Teaching and Learning Award, a 2011 UT System Regents’ Outstanding Teaching Award, and the MAA Southwestern Section’s 2010 Distinguished Teaching Award.This interview took place via email from January 17–February 16, 2020. Photo courtesy of Lauren Davis.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"109 - 119"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1733342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42295879","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 : 2020-01-02DOI: 10.1080/10691898.2020.1720552
Pamela S. Fellers, Shonda Kuiper
Abstract Increasingly students, particularly those in the social sciences, work with survey data collected through a more complex sampling method than a simple random sample. Failing to understand how to properly approach survey data can lead to inaccurate results. In this article, we describe a series of online data visualization applications and corresponding student lab activities designed to help students and teachers of statistics better understand survey design and analysis. The introductory and advanced materials presented are designed to focus on a conceptual understanding of survey data and provide an awareness of the challenges and potential misuse of survey data. Suggestions and examples of how to incorporate these materials are also included. Supplementary materials for this article are available online.
{"title":"Introducing Undergraduates to Concepts of Survey Data Analysis","authors":"Pamela S. Fellers, Shonda Kuiper","doi":"10.1080/10691898.2020.1720552","DOIUrl":"https://doi.org/10.1080/10691898.2020.1720552","url":null,"abstract":"Abstract Increasingly students, particularly those in the social sciences, work with survey data collected through a more complex sampling method than a simple random sample. Failing to understand how to properly approach survey data can lead to inaccurate results. In this article, we describe a series of online data visualization applications and corresponding student lab activities designed to help students and teachers of statistics better understand survey design and analysis. The introductory and advanced materials presented are designed to focus on a conceptual understanding of survey data and provide an awareness of the challenges and potential misuse of survey data. Suggestions and examples of how to incorporate these materials are also included. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"18 - 24"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1720552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45928776","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 : 2020-01-02DOI: 10.1080/10691898.2019.1696257
Victoria Woodard, Hollylynne S. Lee, R. Woodard
Abstract One of the main goals of statistics is to use data to provide evidence in support of an argument. This article will discuss some popular forms of writing assessments currently in use, to demonstrate the differences between the methods for structuring the students’ learning to support their arguments with evidence. We share a model, which was originally created to assess students in introductory statistics and has been adapted for the second course in statistics, which takes a unique approach toward assessing the students’ understanding of statistical concepts through writing. In this model, students are expected to answer prompts that required them to (1) take a stance on an argument, (2) defend their position with facts given in the prompt, (3) discern the implications that those facts implied, and (4) give a proper conclusion to their argument. We provide examples of a few of the writing assignment prompts used in the course, their intended assessment purpose, and common answers that students gave to these assignments. Supplementary materials for this article are available online.
{"title":"Writing Assignments to Assess Statistical Thinking","authors":"Victoria Woodard, Hollylynne S. Lee, R. Woodard","doi":"10.1080/10691898.2019.1696257","DOIUrl":"https://doi.org/10.1080/10691898.2019.1696257","url":null,"abstract":"Abstract One of the main goals of statistics is to use data to provide evidence in support of an argument. This article will discuss some popular forms of writing assessments currently in use, to demonstrate the differences between the methods for structuring the students’ learning to support their arguments with evidence. We share a model, which was originally created to assess students in introductory statistics and has been adapted for the second course in statistics, which takes a unique approach toward assessing the students’ understanding of statistical concepts through writing. In this model, students are expected to answer prompts that required them to (1) take a stance on an argument, (2) defend their position with facts given in the prompt, (3) discern the implications that those facts implied, and (4) give a proper conclusion to their argument. We provide examples of a few of the writing assignment prompts used in the course, their intended assessment purpose, and common answers that students gave to these assignments. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"32 - 44"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1696257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47647163","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 : 2020-01-02DOI: 10.1080/10691898.2020.1730733
S. Muir, L. Tirlea, B. Elphinstone, M. Huynh
Abstract The use of online student response systems (OSRSs) is increasing within tertiary education providers, however, research investigating their potential to enhance student engagement is limited. The aim of the current study was to examine the impact of an OSRS using an experimental crossover design. Quantitative data measuring student engagement was compared from pre- to post-intervention. A qualitative analysis was used to further investigate student perceptions of the OSRS. The results from this study suggest that OSRSs may be appropriate tools to increase student engagement in undergraduate statistics classes. Despite no significant change in engagement scores observed when students were exposed to the OSRS than when they were not, students appreciated the novelty of the OSRS and perceived it to have had a positive impact on their learning experience. Suggestions for how to exploit the advantages of OSRSs and directions for further research are discussed.
{"title":"Promoting Classroom Engagement Through the Use of an Online Student Response System: A Mixed Methods Analysis","authors":"S. Muir, L. Tirlea, B. Elphinstone, M. Huynh","doi":"10.1080/10691898.2020.1730733","DOIUrl":"https://doi.org/10.1080/10691898.2020.1730733","url":null,"abstract":"Abstract The use of online student response systems (OSRSs) is increasing within tertiary education providers, however, research investigating their potential to enhance student engagement is limited. The aim of the current study was to examine the impact of an OSRS using an experimental crossover design. Quantitative data measuring student engagement was compared from pre- to post-intervention. A qualitative analysis was used to further investigate student perceptions of the OSRS. The results from this study suggest that OSRSs may be appropriate tools to increase student engagement in undergraduate statistics classes. Despite no significant change in engagement scores observed when students were exposed to the OSRS than when they were not, students appreciated the novelty of the OSRS and perceived it to have had a positive impact on their learning experience. Suggestions for how to exploit the advantages of OSRSs and directions for further research are discussed.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"25 - 31"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1730733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46286466","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 : 2020-01-02DOI: 10.1080/10691898.2020.1730734
Benjamin S. Baumer, Andrew Bray, Mine Çetinkaya-Rundel, Johanna S. Hardin
Abstract We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues raised should be relevant to nearly all statistics instructors. Supplementary materials for this article are available online.
{"title":"Teaching Introductory Statistics with DataCamp","authors":"Benjamin S. Baumer, Andrew Bray, Mine Çetinkaya-Rundel, Johanna S. Hardin","doi":"10.1080/10691898.2020.1730734","DOIUrl":"https://doi.org/10.1080/10691898.2020.1730734","url":null,"abstract":"Abstract We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues raised should be relevant to nearly all statistics instructors. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"89 - 97"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1730734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43349400","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 : 2020-01-02DOI: 10.1080/10691898.2020.1739501
J. Witmer
{"title":"Note From the Editor","authors":"J. Witmer","doi":"10.1080/10691898.2020.1739501","DOIUrl":"https://doi.org/10.1080/10691898.2020.1739501","url":null,"abstract":"","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"1 - 1"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1739501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49185794","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 : 2020-01-02DOI: 10.1080/10691898.2020.1720551
Stacey A. Hancock, Wendy Rummerfield
Abstract Sampling distributions are fundamental to an understanding of statistical inference, yet research shows that students in introductory statistics courses tend to have multiple misconceptions of this important concept. A common instructional method used to address these misconceptions is computer simulation, often preceded by hands-on simulation activities. However, the results on computer simulation activities’ effects on student understanding of sampling distributions, and if hands-on simulation activities are necessary, are mixed. In this article, we describe an empirical intervention study in which each of eight discussion sections of an introductory statistics course at a large research university was assigned to one of two in-class activity sequences on sampling distributions: one consisting of computer simulation activities preceded by hands-on simulation using dice, cards, or tickets, and the other comprised of computer simulation alone with the same time-on-task. Using a longitudinal model of changes in standardized exam scores across three exams, we found significant evidence that students who took part in a hands-on activity before computer simulation had better improvement from the first midterm to the final exam, on average, compared to those who only did computer simulations. Supplementary materials for this article are available online.
{"title":"Simulation Methods for Teaching Sampling Distributions: Should Hands-on Activities Precede the Computer?","authors":"Stacey A. Hancock, Wendy Rummerfield","doi":"10.1080/10691898.2020.1720551","DOIUrl":"https://doi.org/10.1080/10691898.2020.1720551","url":null,"abstract":"Abstract Sampling distributions are fundamental to an understanding of statistical inference, yet research shows that students in introductory statistics courses tend to have multiple misconceptions of this important concept. A common instructional method used to address these misconceptions is computer simulation, often preceded by hands-on simulation activities. However, the results on computer simulation activities’ effects on student understanding of sampling distributions, and if hands-on simulation activities are necessary, are mixed. In this article, we describe an empirical intervention study in which each of eight discussion sections of an introductory statistics course at a large research university was assigned to one of two in-class activity sequences on sampling distributions: one consisting of computer simulation activities preceded by hands-on simulation using dice, cards, or tickets, and the other comprised of computer simulation alone with the same time-on-task. Using a longitudinal model of changes in standardized exam scores across three exams, we found significant evidence that students who took part in a hands-on activity before computer simulation had better improvement from the first midterm to the final exam, on average, compared to those who only did computer simulations. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"17 - 9"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1720551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43989913","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 : 2020-01-02DOI: 10.1080/10691898.2019.1704201
Sydney Lawton, Laura Taylor
Abstract This article presents the results of a case study from one professor’s experience teaching an introductory statistics course. The goal of this study was to better understand student perceptions of engagement in a statistics course. Voluntary, self-reported data were collected daily for students to evaluate the engagement level of the class that day, and students also identified activities that they considered engaging. A final survey was administered at the end of the semester to provide a holistic, retrospective measure of engagement in the course and to collect feedback on various questions related to perceptions of engagement. Results indicate variation in student engagement scores and variation in engagement scores across the semester indicating some influence of class activity on perceptions of engagement. Perceptions of engagement are contextualized with students’ comments from the daily surveys. Associations between engagement and final course grade were also investigated. Student perceptions of engagement were also compared to the professor’s perception of engagement for students.
{"title":"Student Perceptions of Engagement in an Introductory Statistics Course","authors":"Sydney Lawton, Laura Taylor","doi":"10.1080/10691898.2019.1704201","DOIUrl":"https://doi.org/10.1080/10691898.2019.1704201","url":null,"abstract":"Abstract This article presents the results of a case study from one professor’s experience teaching an introductory statistics course. The goal of this study was to better understand student perceptions of engagement in a statistics course. Voluntary, self-reported data were collected daily for students to evaluate the engagement level of the class that day, and students also identified activities that they considered engaging. A final survey was administered at the end of the semester to provide a holistic, retrospective measure of engagement in the course and to collect feedback on various questions related to perceptions of engagement. Results indicate variation in student engagement scores and variation in engagement scores across the semester indicating some influence of class activity on perceptions of engagement. Perceptions of engagement are contextualized with students’ comments from the daily surveys. Associations between engagement and final course grade were also investigated. Student perceptions of engagement were also compared to the professor’s perception of engagement for students.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"45 - 55"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1704201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43482219","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 : 2020-01-02DOI: 10.1080/10691898.2020.1713936
Kevin Cummiskey, Bryan Adams, J. Pleuss, Dusty S Turner, Nicholas J. Clark, Krista L. Watts
Abstract Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.
{"title":"Causal Inference in Introductory Statistics Courses","authors":"Kevin Cummiskey, Bryan Adams, J. Pleuss, Dusty S Turner, Nicholas J. Clark, Krista L. Watts","doi":"10.1080/10691898.2020.1713936","DOIUrl":"https://doi.org/10.1080/10691898.2020.1713936","url":null,"abstract":"Abstract Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"2 - 8"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1713936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43979052","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 : 2019-10-13DOI: 10.1080/10691898.2020.1817815
Jingchen Hu
Abstract We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students’ Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students’ understanding of the methods. Collaborative case studies further enrich students’ learning and provide experience to solve open-ended applied problems. The course has an emphasis on undergraduate research, where accessible academic journal articles are read, discussed, and critiqued in class. With increased confidence and familiarity, students take the challenge of reading, implementing, and sometimes extending methods in journal articles for their course projects. Supplementary materials for this article are available online.
{"title":"A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research","authors":"Jingchen Hu","doi":"10.1080/10691898.2020.1817815","DOIUrl":"https://doi.org/10.1080/10691898.2020.1817815","url":null,"abstract":"Abstract We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students’ Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students’ understanding of the methods. Collaborative case studies further enrich students’ learning and provide experience to solve open-ended applied problems. The course has an emphasis on undergraduate research, where accessible academic journal articles are read, discussed, and critiqued in class. With increased confidence and familiarity, students take the challenge of reading, implementing, and sometimes extending methods in journal articles for their course projects. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"229 - 235"},"PeriodicalIF":2.2,"publicationDate":"2019-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1817815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44362075","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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