Pub Date : 2020-05-03DOI: 10.1080/10691898.2020.1787115
Nicola Justice, Laura J Le, A. Sabbag, Elizabeth Fry, Laura E. Ziegler, Joan Garfield
Abstract One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.
{"title":"The CATALST Curriculum: A Story of Change","authors":"Nicola Justice, Laura J Le, A. Sabbag, Elizabeth Fry, Laura E. Ziegler, Joan Garfield","doi":"10.1080/10691898.2020.1787115","DOIUrl":"https://doi.org/10.1080/10691898.2020.1787115","url":null,"abstract":"Abstract One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"175 - 186"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1787115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44245905","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 : 2020-05-03DOI: 10.1080/10691898.2020.1756542
Abstract Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience.
{"title":"Mentoring Undergraduate Research in Statistics: Reaping the Benefits and Overcoming the Barriers","authors":"","doi":"10.1080/10691898.2020.1756542","DOIUrl":"https://doi.org/10.1080/10691898.2020.1756542","url":null,"abstract":"Abstract Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"59 14","pages":"140 - 153"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141207215","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 : 2020-05-03DOI: 10.1080/10691898.2020.1765710
Summer Bateiha, Hope Marchionda, Melanie Autin
Abstract Many students who enroll in introductory statistics courses do not have positive attitudes about the subject. A 2012 wide-ranging study by Schau and Emmioglu showed that student attitudes do not tend to improve after completing an introductory statistics course. However, there is a need for more studies about attitudes in introductory statistics courses that utilize reform teaching methods. In this article, we present findings about student attitudes toward statistics in both a teacher-centered lecture-based class and a student-centered active learning class, taught by the same instructor. The overall results of this study were consistent with those reported in the study by Schau and Emmioğlu. Although on an overall level, it seemed that attitudes did not change for both classes, when each attitude component was analyzed on a deeper level, from both a quantitative and a qualitative perspective, differences were found between the two classes for the components of Effort, Affect and Cognitive Competence, Interest, and Difficulty.
{"title":"Teaching Style and Attitudes: A Comparison of Two Collegiate Introductory Statistics Classes","authors":"Summer Bateiha, Hope Marchionda, Melanie Autin","doi":"10.1080/10691898.2020.1765710","DOIUrl":"https://doi.org/10.1080/10691898.2020.1765710","url":null,"abstract":"Abstract Many students who enroll in introductory statistics courses do not have positive attitudes about the subject. A 2012 wide-ranging study by Schau and Emmioglu showed that student attitudes do not tend to improve after completing an introductory statistics course. However, there is a need for more studies about attitudes in introductory statistics courses that utilize reform teaching methods. In this article, we present findings about student attitudes toward statistics in both a teacher-centered lecture-based class and a student-centered active learning class, taught by the same instructor. The overall results of this study were consistent with those reported in the study by Schau and Emmioğlu. Although on an overall level, it seemed that attitudes did not change for both classes, when each attitude component was analyzed on a deeper level, from both a quantitative and a qualitative perspective, differences were found between the two classes for the components of Effort, Affect and Cognitive Competence, Interest, and Difficulty.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"154 - 164"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1765710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48491258","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 : 2020-05-03DOI: 10.1080/10691898.2020.1781003
Yael Tal, Ida Kukliansky
Abstract The aim of this study is to explore the judgments and reasoning in probabilistic tasks that require comparing two probabilities either with or without introducing an additional degree of uncertainty. The reasoning associated with the task having an additional condition of uncertainty has not been discussed in previous studies. The 66 undergraduate students, participants in this study, used an analytic process for the task without an additional condition of uncertainty and a heuristic for the task with it. The findings show that they focused on the most likely event and derived a prediction based on this event that, in some cases, led them to answer incorrectly. The educational implications include a gradual method for developing better intuition for the students to help them tackle similar problems in the future.
{"title":"Reasoning Under Uncertainty: Maximum Likelihood Heuristic in a Problem With a Random Transfer","authors":"Yael Tal, Ida Kukliansky","doi":"10.1080/10691898.2020.1781003","DOIUrl":"https://doi.org/10.1080/10691898.2020.1781003","url":null,"abstract":"Abstract The aim of this study is to explore the judgments and reasoning in probabilistic tasks that require comparing two probabilities either with or without introducing an additional degree of uncertainty. The reasoning associated with the task having an additional condition of uncertainty has not been discussed in previous studies. The 66 undergraduate students, participants in this study, used an analytic process for the task without an additional condition of uncertainty and a heuristic for the task with it. The findings show that they focused on the most likely event and derived a prediction based on this event that, in some cases, led them to answer incorrectly. The educational implications include a gradual method for developing better intuition for the students to help them tackle similar problems in the future.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"187 - 196"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1781003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43797559","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 : 2020-05-03DOI: 10.1080/10691898.2020.1776653
Anna E. Bargagliotti, Wendy Binder, Lance Blakesley, Zaki Eusufzai, Ben Fitzpatrick, Máire B Ford, Karen Huchting, Suzanne Larson, N. Miric, Robert J. Rovetti, K. Seal, T. Zachariah
Abstract It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines.
{"title":"Undergraduate Learning Outcomes for Achieving Data Acumen","authors":"Anna E. Bargagliotti, Wendy Binder, Lance Blakesley, Zaki Eusufzai, Ben Fitzpatrick, Máire B Ford, Karen Huchting, Suzanne Larson, N. Miric, Robert J. Rovetti, K. Seal, T. Zachariah","doi":"10.1080/10691898.2020.1776653","DOIUrl":"https://doi.org/10.1080/10691898.2020.1776653","url":null,"abstract":"Abstract It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"197 - 211"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1776653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42259158","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 : 2020-05-03DOI: 10.1080/10691898.2020.1752859
Karsten Lübke, Matthias Gehrke, Jörg Horst, G. Szepannek
Abstract Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.
{"title":"Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data","authors":"Karsten Lübke, Matthias Gehrke, Jörg Horst, G. Szepannek","doi":"10.1080/10691898.2020.1752859","DOIUrl":"https://doi.org/10.1080/10691898.2020.1752859","url":null,"abstract":"Abstract Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"133 - 139"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1752859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47658946","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 : 2020-05-03DOI: 10.1080/10691898.2020.1780173
Carole L. Birrell
Abstract Sample survey design is a topic usually taught to students undertaking a minor or major in statistics in the latter part of their bachelor’s degree. This article describes an assessment project that fosters active learning and helps to develop a set of essential skills for statistical practice. The project is completed in pairs and submitted in two parts. This allows feedback from the first part to be acted upon for the second part. Ideally, students would gain experience sampling from an actual population. However, the time involved in obtaining approval from the university’s ethics committee may not be feasible for a short course. An alternative is to use an online virtual population such as the Islands, which provides students with an experience in setting up a sampling frame, requesting consent from potential participants, and collecting data. Proficiency in written communication and teamwork are highly valued by employers of statistics graduates. This project encourages collaborative learning in the design of the sample survey, statistical analysis of data collected, and the development of a final written report. It can easily be adapted for first year students and also be extended to suit Honors or Masters level students.
{"title":"Teaching Sample Survey Design—A Project Using a Virtual Population","authors":"Carole L. Birrell","doi":"10.1080/10691898.2020.1780173","DOIUrl":"https://doi.org/10.1080/10691898.2020.1780173","url":null,"abstract":"Abstract Sample survey design is a topic usually taught to students undertaking a minor or major in statistics in the latter part of their bachelor’s degree. This article describes an assessment project that fosters active learning and helps to develop a set of essential skills for statistical practice. The project is completed in pairs and submitted in two parts. This allows feedback from the first part to be acted upon for the second part. Ideally, students would gain experience sampling from an actual population. However, the time involved in obtaining approval from the university’s ethics committee may not be feasible for a short course. An alternative is to use an online virtual population such as the Islands, which provides students with an experience in setting up a sampling frame, requesting consent from potential participants, and collecting data. Proficiency in written communication and teamwork are highly valued by employers of statistics graduates. This project encourages collaborative learning in the design of the sample survey, statistical analysis of data collected, and the development of a final written report. It can easily be adapted for first year students and also be extended to suit Honors or Masters level students.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"165 - 174"},"PeriodicalIF":2.2,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1780173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49346486","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 : 2020-02-22DOI: 10.1080/10691898.2020.1847008
J. Albert, Jingchen Hu
Abstract Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an overview of the various options for implementing Bayesian computational methods motivated to achieve particular learning outcomes. For each computational method, we propose activities and exercises, and discuss each method’s pedagogical advantages and disadvantages based on our experience in the classroom. The goal is to present guidance on the choice of computation for the instructors who are introducing Bayesian methods in their undergraduate statistics curriculum. Supplementary materials for this article are available online.
{"title":"Bayesian Computing in the Undergraduate Statistics Curriculum","authors":"J. Albert, Jingchen Hu","doi":"10.1080/10691898.2020.1847008","DOIUrl":"https://doi.org/10.1080/10691898.2020.1847008","url":null,"abstract":"Abstract Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an overview of the various options for implementing Bayesian computational methods motivated to achieve particular learning outcomes. For each computational method, we propose activities and exercises, and discuss each method’s pedagogical advantages and disadvantages based on our experience in the classroom. The goal is to present guidance on the choice of computation for the instructors who are introducing Bayesian methods in their undergraduate statistics curriculum. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"236 - 247"},"PeriodicalIF":2.2,"publicationDate":"2020-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1847008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44988123","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: 10.1080/10691898.2019.1705943
Rafiq Hijazi, I. Alfaki
Abstract This article is the first to thoroughly investigate the state of undergraduate statistics education in the Arab world. It discusses evidence with respect to the quality of education in general and statistics education in particular. Based on a survey of statistics programs in Arab universities, several issues pertaining to curriculum structure, pedagogical practices, and matching learning outcomes with labor market needs are discussed. The survey results reveal a gap between the undergraduate statistics programs in Arab universities and the international curriculum guidelines. This gap signals the urgent need for reforming and enhancing statistics education to address the needs of the labor market in this era of information. Recommendations and strategic reforms based on best international practices are outlined.
{"title":"Reforming Undergraduate Statistics Education in the Arab World in the Era of Information","authors":"Rafiq Hijazi, I. Alfaki","doi":"10.1080/10691898.2019.1705943","DOIUrl":"https://doi.org/10.1080/10691898.2019.1705943","url":null,"abstract":"Abstract This article is the first to thoroughly investigate the state of undergraduate statistics education in the Arab world. It discusses evidence with respect to the quality of education in general and statistics education in particular. Based on a survey of statistics programs in Arab universities, several issues pertaining to curriculum structure, pedagogical practices, and matching learning outcomes with labor market needs are discussed. The survey results reveal a gap between the undergraduate statistics programs in Arab universities and the international curriculum guidelines. This gap signals the urgent need for reforming and enhancing statistics education to address the needs of the labor market in this era of information. Recommendations and strategic reforms based on best international practices are outlined.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"75 - 88"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1705943","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46681245","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: 10.1080/10691898.2020.1721035
Daniel Adrian, D. Reischman, Kirk Anderson, Mary Richardson, P. Stephenson
ABSTRACT Maps are a primary method of displaying statistical data that comes from a geographical frame. Maps are esthetically appealing and make it easier to identify geographic patterns in a dataset. However, few introductory statistical texts and courses explicitly present maps as a way to display data. In this article, we will present examples of different types of statistical maps and illustrate how these maps can be used in the instruction of an introductory statistics course.
{"title":"Helping Introductory Statistics Students Find Their Way Using Maps","authors":"Daniel Adrian, D. Reischman, Kirk Anderson, Mary Richardson, P. Stephenson","doi":"10.1080/10691898.2020.1721035","DOIUrl":"https://doi.org/10.1080/10691898.2020.1721035","url":null,"abstract":"ABSTRACT Maps are a primary method of displaying statistical data that comes from a geographical frame. Maps are esthetically appealing and make it easier to identify geographic patterns in a dataset. However, few introductory statistical texts and courses explicitly present maps as a way to display data. In this article, we will present examples of different types of statistical maps and illustrate how these maps can be used in the instruction of an introductory statistics course.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"28 1","pages":"56 - 74"},"PeriodicalIF":2.2,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2020.1721035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43503552","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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